diff --git a/.flake8 b/.flake8
new file mode 100644
index 00000000..d26ea701
--- /dev/null
+++ b/.flake8
@@ -0,0 +1,3 @@
+[flake8]
+max-line-length = 120
+exclude = vel/openai, test, vel/api/__init__.py, vel/rl/api/__init__.py, vel/data/__init__.py, vel/metric/__init__.py, vel/metric/base/__init__.py, vel/train/__init__.py, vel/optimizer/ranger.py, vel/optimizer/radam.py
diff --git a/.gitignore b/.gitignore
index b0a800fd..e0b8259c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -117,3 +117,6 @@ environment.yaml
 
 # Test cache
 /.pytest_cache
+
+# WANDB settings
+/wandb
diff --git a/.velproject.yaml b/.velproject.yaml
index 5921e596..b9ba7d20 100644
--- a/.velproject.yaml
+++ b/.velproject.yaml
@@ -1,14 +1,21 @@
+project_name: 'vel'
+
 storage:
   name: vel.storage.classic
 
   backend:
-    name: vel.storage.backend.mongodb
-    uri: 'mongodb://localhost:27017/'
-    database: deep_learning
+    name: vel.storage.backend.dummy
+
+#    Other potential setting
+#    name: vel.storage.backend.mongodb
+#    uri: 'mongodb://localhost:27017/'
+#    database: deep_learning
 
   streaming:
-    - name: vel.storage.streaming.visdom
+    - name: vel.storage.streaming.tensorboard
     - name: vel.storage.streaming.stdout
+#    - name: vel.storage.streaming.visdom
+#    - name: vel.storage.streaming.wandb
 
 
 checkpoint_strategy:
@@ -20,3 +27,11 @@ visdom_settings:
   server: 'http://localhost'
   port: 8097
 
+
+# List of commands that are shared among all models in this project
+global_commands:
+  list:
+    name: vel.command.list_command
+  summary:
+    name: vel.command.summary_command
+
diff --git a/Makefile b/Makefile
index e26492f2..87c23a87 100644
--- a/Makefile
+++ b/Makefile
@@ -1,3 +1,7 @@
+.PHONY: default test partest requpgrade lint
+
+default: test;
+
 tag := $(shell git symbolic-ref -q --short HEAD)
 
 docker-build:
@@ -30,4 +34,10 @@ serve-visdom:
 	python -m visdom.server
 
 test:
-	pytest .
\ No newline at end of file
+	pytest .
+
+requirements.txt: requirements.in
+	pip-compile --upgrade requirements.in
+
+lint:
+	flake8 vel
diff --git a/README.md b/README.md
index f92438cf..29681425 100644
--- a/README.md
+++ b/README.md
@@ -1,22 +1,21 @@
-# Vel 0.3
+# Vel 0.4
 
 [![Build Status](https://travis-ci.org/MillionIntegrals/vel.svg?branch=master)](https://travis-ci.org/MillionIntegrals/vel)
 [![PyPI version](https://badge.fury.io/py/vel.svg)](https://badge.fury.io/py/vel)
 [![GitHub](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/MillionIntegrals/vel/blob/master/LICENSE)
-[![Gitter chat](https://badges.gitter.im/MillionIngegrals/vel.png)](https://gitter.im/deep-learning-vel)
 
 
 Bring **velocity** to deep-learning research.
 
 
 This project hosts a collection of **highly modular** deep learning components that are tested to be working well together.
-A simple yaml-based system ties these modules together declaratively using configuration files,
-but everything that can be defined using config files can be coded directly in the python script as well.
+A simple yaml-based system ties these modules declaratively using configuration files.
 
 
-This is still an early version and a hobby project so documentation is unfortunately nonexistent. I've tried to make the
-code as clear as possible, and provide many usage examples, but whenever there was a tradeoff to be made between 
-simplicity and modularity I've chosen modularity first and simplicity second.
+This is still an early version and a hobby project, so documentation is unfortunately nonexistent.
+I've made an effort to make the code clear and provide many usage examples,
+but whenever there was a tradeoff to be made between simplicity and modularity
+I've chosen modularity first and simplicity second. Therefore, high emphasis is made on interfaces between components.
 
 
 Having conducted a few research projects, I've gathered a small collection of repositories 
@@ -40,6 +39,9 @@ If that's not the case few bits of custom glue code should do the job.
 This repository is still in an early stage of that journey but it will grow
 as I'll be putting work into it.
 
+For up-to-date benchmarks, look here:
+[Benchmarks](docs/Benchmarks.md)
+
 
 # Blogposts
 
@@ -55,16 +57,14 @@ pip install -e .
 ```
 from the repository root directory.
 
-This project requires Python at least 3.6 and PyTorch 1.0.
+This project requires Python at least 3.6 and PyTorch 1.3.
 If you want to run YAML config examples, you'll also need a **project configuration file**
 `.velproject.yaml`. An example is included in this repository.
 
-Default project configuration writes
-metrics to MongoDB instance open on localhost port 27017 and Visdom instance 
-on localhost port 8097. 
+Default project configuration writes logs to the tensorboard directory `output/tensorboard`
+under the main directory. Output modules to visdom, mongodb and wandb are also implemented.
 
-If you don't want to run these services, there is included
-another example file `.velproject.dummy.yaml`
+If you don't want any logging, there is included another example file `.velproject.dummy.yaml`
 that writes training progress to the standard output only.
 To use it, just rename it to `.velproject.yaml`.
 
@@ -80,6 +80,7 @@ To use it, just rename it to `.velproject.yaml`.
   understand what exactly the model is doing for newcomers already comfortable with PyTorch. 
 - All state-of-the-art models should be implemented in the framework with accuracy
   matching published results.
+  For up-to-date benchmarks, look here: [Benchmarks](docs/Benchmarks.md)
 - All common deep learning workflows should be fast to implement, while 
   uncommon ones should be possible, at least as far as PyTorch allows.
   
@@ -89,7 +90,7 @@ To use it, just rename it to `.velproject.yaml`.
 Several models are already implemented in the framework and have example config files
 that are ready to run and easy to modify for other similar usecases:
 
-- State-of-the art results on Cifar10 dataset using residual networks
+- Residual networks (resnets) trained on Cifar10 dataset replicating published performance
 - Cats vs dogs classification using transfer learning from a resnet34 model pretrained on 
   ImageNet
   
@@ -101,14 +102,14 @@ that are ready to run and easy to modify for other similar usecases:
   
 # Implemented models - Reinforcement learning
 
-- Continuous and discrete action spaces
-- Basic support for LSTM policies for A2C and PPO
-- Following published policy gradient reinforcement learning algorithms:
+- Support for continuous and discrete environment action spaces
+- Basic support for recurrent policies for A2C and PPO
+- Following policy gradient reinforcement learning algorithms:
     - Advantage Actor-Critic (A2C)
-    - Deep Deterministic Policy Gradient (DDPG)
     - Proximal Policy Optimization (PPO)
     - Trust Region Policy Optimization (TRPO)
     - Actor-Critic with Experience Replay (ACER)
+    - Deep Deterministic Policy Gradient (DDPG)
 - Deep Q-Learning (DQN) as described by DeepMind in their Nature publication with following 
   improvements:
     - Double DQN
@@ -118,6 +119,14 @@ that are ready to run and easy to modify for other similar usecases:
     - Distributional Q-Learning
     - Noisy Networks for Exploration
     - Rainbow (combination of the above)
+    
+# Implemented models - Unsupervised learning
+
+- A simple AutoEncoder (AE) with example on MNIST dataset.
+- Latent variable models:
+    - Variational AutoEncoders (VAE)
+    - Importance Weighted AutoEncoder (IWAE)
+    - Vector-Quantised Variational AutoEncoder (VQ-VAE)
 
 
 # Examples
@@ -128,14 +137,14 @@ Most of the examples for this framework are defined using config files in the
 For example, to run the A2C algorithm on a Breakout atari environment, simply invoke:
 
 ```
-python -m vel.launcher examples-configs/rl/atari/a2c/breakout_a2c.yaml train
+python -m vel.launcher examples-configs/rl/atari/atari_a2c.yaml train
 ```
 
 If you install the library locally, you'll have a special wrapper created
 that will invoke the launcher for you. Then, above becomes:
 
 ```
-vel examples-configs/rl/atari/a2c/breakout_a2c.yaml train
+vel examples-configs/rl/atari/atari_a2c.yaml train
 ```
 
 General command line interface of the launcher is:
@@ -152,112 +161,6 @@ If you prefer to use the library from inside your scripts, take a look at the
 well. Scripts generally don't require any MongoDB or Visdom setup, so they can be run straight
 away in any setup, but their output will be less rich and less informative.
 
-Here is an example script running the same setup as a config file from above:
-
-```python
-import torch
-import torch.optim as optim
-
-from vel.rl.metrics import EpisodeRewardMetric
-from vel.storage.streaming.stdout import StdoutStreaming
-from vel.util.random import set_seed
-
-from vel.rl.env.classic_atari import ClassicAtariEnv
-from vel.rl.vecenv.subproc import SubprocVecEnvWrapper
-
-from vel.modules.input.image_to_tensor import ImageToTensorFactory
-from vel.rl.models.stochastic_policy_model import StochasticPolicyModelFactory
-from vel.rl.models.backbone.nature_cnn import NatureCnnFactory
-
-
-from vel.rl.reinforcers.on_policy_iteration_reinforcer import (
-    OnPolicyIterationReinforcer, OnPolicyIterationReinforcerSettings
-)
-
-from vel.rl.algo.policy_gradient.a2c import A2CPolicyGradient
-from vel.rl.env_roller.step_env_roller import StepEnvRoller
-
-from vel.api.info import TrainingInfo, EpochInfo
-
-
-def breakout_a2c():
-    device = torch.device('cuda:0')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Create 16 environments evaluated in parallel in sub processess with all usual DeepMind wrappers
-    # These are just helper functions for that
-    vec_env = SubprocVecEnvWrapper(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=16, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model = StochasticPolicyModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    ).instantiate(action_space=vec_env.action_space)
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = OnPolicyIterationReinforcer(
-        device=device,
-        settings=OnPolicyIterationReinforcerSettings(
-            batch_size=256,
-            number_of_steps=5,
-        ),
-        model=model,
-        algo=A2CPolicyGradient(
-            entropy_coefficient=0.01,
-            value_coefficient=0.5,
-            max_grad_norm=0.5,
-            discount_factor=0.99,
-        ),
-        env_roller=StepEnvRoller(
-            environment=vec_env,
-            device=device,
-        )
-    )
-
-    # Model optimizer
-    optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=7.0e-4, eps=1e-3)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[StdoutStreaming()]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = int(1.1e7 / (5 * 16) / 100)
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=100,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info)
-
-    training_info.on_train_end()
-
-
-if __name__ == '__main__':
-    breakout_a2c()
-```
-
 # Docker
 
 Dockerized version of this library is available in from the Docker Hub as
@@ -307,17 +210,66 @@ Very likely to be included:
 
 
 Possible to be included:
-- Popart reward normalization
-- Parameter Space Noise for Exploration
-- Hindsight experience replay
 - Generative adversarial networks
 
+For version 0.5 I'll again be looking to expand widely on the spectrum of available models in the framework,
+as well as I'll try to support **multi-gpu** training by data parallelism.
 
-Code quality:
-- Rename models to policies
-- Force dictionary inputs and outputs for policies
-- Factor action noise back into the policy
-- Use linter as a part of the build process
+Work in progress roadmap:
+
+- Popart reward normalization
+- PixelCNN
+- MADE: Masked Autoencoder for Distribution Estimation
+- Variational AutoEncoder with Inverse Autoregressive Flow
+
+
+# Directories
+
+Below I'll list brief explanation about contents of main top-level directories.
+
+- `docs` - Few markdown documents about the framework
+- `examples-configs` - Ready to run configs with tried and tested models, usually heavily inspired by existing
+ literature.
+- `examples-notebooks` - A few examples of how to interact with `vel` from the level of IPython notebook
+- `vel` - Root for the Python source of the package
+  - `vel.api` - Interfaces and base classes used all over the codebase. To be used in source code only and not 
+    referenced from config files.
+  - `vel.callback` - Definitions of callbacks that can be used in the training process. Can be referenced both by code
+  and by the config files.
+  - `vel.command` - Commands that can be used in your configuration files, and there isn't much need to refer to
+  them from code.
+  - `vel.data` - Various classes for handling data sources and data transformations. Referenced both by source code
+   and config files.
+  - `vel.function` - Interfaces for creating various functions/interpolators, to be refereced by config files.
+  - `vel.internal` - Functions and classes to be only used by `vel` internally, and not by by user code nor configs.
+  - `vel.metric` - Code for tracking metrics during training of your models. To be used by both code and configs.
+  - `vel.model` - Definition of models, which is kind of an end-package that references all other packages. Models
+  contain most other parts of the pipeline and define a training procedure.
+  - `vel.module` - Various useful definitions of PyTorch modules, to be used when defining your own `models` and
+  `layers`.
+  - `vel.net` - "Network" module that may be referenced by a model to define neural network architecture used.
+      - `vel.net.layer` - Modular layer system for defining networks declaratively in configuration files.
+  - `vel.notebook` - Utilities for interfacing with `vel` using IPython notebooks
+  - `vel.openai` - Imported parts of the codebase of `openai/baselines` that I didn't want to bring as a package
+  dependency. To be referenced in code mostly.
+  - `vel.optimizer` - Various implementations of deep learning optimizers. To be referenced mostly by scripts.
+  - `vel.rl` - Meta package for everything related to Reinforcement Learning
+    - `vel.rl.api` - Interfaces and base classes to be used for Reinforcement Learning models and other classes.
+    - `vel.rl.buffer` - All classes relating to experience replay and experience buffers
+    - `vel.rl.command` - Commands used for RL training
+    - `vel.rl.env` - Basic reinforcement learning environments, mostly based on OpenAI gym
+    - `vel.rl.env_roller` - Classes for generating environment rollouts
+    - `vel.rl.layer` - Layers desined especially for RL
+    - `vel.rl.module` - PyTorch modules designed for RL
+    - `vel.rl.policy` - Equivalent of `vel.model` for RL
+    - `vel.rl.reinforcer` - Reinforcer manages RL training, and corresponds to `Trainer` in Supervised Learning
+    - `vel.rl.vecenv` - Utilities for vectorizing environments and stepping multiple environments at the same time
+  - `vel.scheduler` - Classes helping to set up learning rate schedules for the optimizers. To be referenced mostly
+  by scripts.
+  - `vel.storage` - Everything about persisting models and metrics. To be referenced mostly by configuration files.
+  - `vel.train` - Utilities for defining more generic training loops of models. To be referenced in both code and
+  config.
+  - `vel.util` - Collection of various utility functions to be used by all other modules.
 
 
 # Citing
diff --git a/docs/Benchmarks.md b/docs/Benchmarks.md
new file mode 100644
index 00000000..c6555e69
--- /dev/null
+++ b/docs/Benchmarks.md
@@ -0,0 +1,25 @@
+# Benchmarks
+
+In this file I'll gather up to date benchmarking results for examples included in this repository.
+
+Levels of hierarchy will be first task, then dataset (benchmark) and then table listing model results of
+relevant metrics.
+
+Each metric I'll try to average over six runs and provide mean and standard deviation of results.
+
+
+## Generative models
+
+
+### Binarized MNIST
+
+
+For VAE models, I'll include upper bound for Negative Log Likelihood (NLL) for given number of importance samples (IS).
+
+
+|    Model     | NLL (IS=1)  |NLL (IS=100)|NLL (IS=5000)|
+|-------------:|------------:|-----------:|------------:|
+|        FC VAE| 90.85 ± 0.20|87.00 ± 0.28| 86.83 ± 0.26|
+|FC IWAE (k=50)|100.53 ± 0.62|82.41 ± 0.05| 80.73 ± 0.09|
+|       CNN VAE| 86.47 ± 0.11|81.33 ± 0.05| 81.02 ± 0.05|
+|CNN IWAE (k=5)| 88.44 ± 0.25|78.78 ± 0.05| 77.77 ± 0.06|
diff --git a/docs/Bibliography.md b/docs/Bibliography.md
index 2bcf3548..3453e484 100644
--- a/docs/Bibliography.md
+++ b/docs/Bibliography.md
@@ -4,15 +4,48 @@ Below, I present a most likely incomplete list of works I referred to when I was
 on this library:
 
 
+### Latent variable models
+
+- (Dec 2013) **Auto-Encoding Variational Bayes**
+	Diederik P Kingma, Max Welling
+	https://arxiv.org/abs/1312.6114
+
+
+- (Sep 2015) **Importance Weighted Autoencoders**
+    Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
+    https://arxiv.org/abs/1509.00519
+    
+    
+- (Nov 2017) **Neural Discrete Representation Learning**
+    Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu
+    https://arxiv.org/abs/1711.00937
+
+
 ### Learning rate and optimization
 
 - (2012) **Lecture 6.5 -- RmsProp: Divide the gradient by a running average of its recent magnitude**
   Geoff Hinton
   http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
+  
+- (Dec 2012) **ADADELTA: An Adaptive Learning Rate Method**
+  Matthew D. Zeiler
+  https://arxiv.org/abs/1212.5701
+  
+- (Dec 2014) **Adam: A Method for Stochastic Optimization**
+  Diederik P. Kingma, Jimmy Ba
+  https://arxiv.org/abs/1412.6980
 
 - (Jun 2015) **Cyclical Learning Rates for Training Neural Networks**
   Leslie N. Smith
   https://arxiv.org/abs/1506.01186
+  
+- (Jul 2019) **Lookahead Optimizer: k steps forward, 1 step back**
+  Michael R. Zhang, James Lucas, Geoffrey Hinton, Jimmy Ba
+  https://arxiv.org/abs/1907.08610
+  
+- (Aug 2019) **On the Variance of the Adaptive Learning Rate and Beyond**
+  Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei
+  https://arxiv.org/abs/1908.03265
 
 ### Residual Networks
 
@@ -63,10 +96,19 @@ on this library:
   https://arxiv.org/abs/1707.06887  
   
 - (Oct 2017) **Rainbow: Combining Improvements in Deep Reinforcement Learning**
-  Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver
+  Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan,
+  Bilal Piot, Mohammad Azar, David Silver
   https://arxiv.org/abs/1710.02298
   
   
+### Generative adversarial networks
+
+- (Jun 2014) **Generative Adversarial Networks**
+  Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville,
+  Yoshua Bengio
+  https://arxiv.org/abs/1406.2661
+  
+  
 ### Policy gradient methods
 
 
@@ -120,11 +162,15 @@ on this library:
 ### Open source repositories
 
 This repository contains various parts of functionality derived from open source code
-in the following repositories:
+in the following repositories (in alphabetical order):
 
+- https://github.com/Kaixhin/Rainbow
+- https://github.com/Khrylx/PyTorch-RL
+- https://github.com/LiyuanLucasLiu/RAdam
+- https://github.com/eriklindernoren/PyTorch-GAN
 - https://github.com/fastai/fastai
-- https://github.com/pytorch/pytorch
+- https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
 - https://github.com/openai/baselines
-- https://github.com/tensorflow/tensorflow
-- https://github.com/Kaixhin/Rainbow
-- https://github.com/Khrylx/PyTorch-RL
\ No newline at end of file
+- https://github.com/pytorch/pytorch
+- https://github.com/ritheshkumar95/pytorch-vqvae/
+- https://github.com/tensorflow/tensorflow
\ No newline at end of file
diff --git a/examples-configs/autoencoder/mnist/mnist_cnn_ae.yaml b/examples-configs/autoencoder/mnist/mnist_cnn_ae.yaml
new file mode 100644
index 00000000..897bafaf
--- /dev/null
+++ b/examples-configs/autoencoder/mnist/mnist_cnn_ae.yaml
@@ -0,0 +1,39 @@
+name: 'mnist_cnn_ae'
+
+
+model:
+  name: vel.model.autoencoder.cnn_autoencoder
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  channels: [8, 16, 16]
+  representation_length: 16
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.image_to_tensor
+    - name: vel.data.transformation.unsupervised
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 1.0e-3
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 12
+    log_frequency: 100
+
+    checkpoint:
+      metric: 'val:loss'
diff --git a/examples-configs/classification/cifar10/cifar10_cnn_01.yaml b/examples-configs/classification/cifar10/cifar10_cnn_01.yaml
deleted file mode 100644
index cce9c77b..00000000
--- a/examples-configs/classification/cifar10/cifar10_cnn_01.yaml
+++ /dev/null
@@ -1,37 +0,0 @@
-name: 'cifar10_cnn_01'
-
-
-model:
-  name: vel.models.vision.cifar10_cnn_01
-  img_rows: 32
-  img_cols: 32
-  img_channels: 3
-  num_classes: 10
-
-source:
-  name: vel.sources.vision.cifar10
-#  batch_size: 128
-  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 12
-    log_frequency: 20
-    optimizer:
-      name: vel.optimizers.adadelta
diff --git a/examples-configs/classification/cifar10/cifar10_resnetv1_110.yaml b/examples-configs/classification/cifar10/cifar10_resnetv1_110.yaml
deleted file mode 100644
index a34c34a5..00000000
--- a/examples-configs/classification/cifar10/cifar10_resnetv1_110.yaml
+++ /dev/null
@@ -1,73 +0,0 @@
-name: 'cifar10_resnetv1_110'
-
-
-model:
-  name: vel.models.vision.cifar_resnet_v1
-  img_channels: 3
-  num_classes: 10
-
-  mode: basic # Basic or bottleneck
-  blocks: [18, 18, 18] # ResNet110
-
-source:
-  name: vel.sources.vision.cifar10
-  batch_size: 128
-#  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 200
-    log_frequency: 100
-
-    optimizer:
-      name: vel.optimizers.sgd
-      lr: 0.1
-      weight_decay: 0.0001
-      momentum: 0.9
-
-#    optimizer:
-#      name: vel.optimizers.adam
-#      lr: 0.001
-#      weight_decay: 0.001
-
-#    scheduler:
-#      name: vel.scheduler.multi_step
-#      milestones: [20, 50, 100, 150]
-#      gamma: 0.33
-
-    scheduler:
-      name: vel.scheduler.ladder
-      ladder:
-      - [5, 0.1]  # Special iteration to kickstart convergence
-      - [75, 1.0]
-      - [40, 0.1]
-      - [80, 0.01]
-
-#    scheduler:
-#      name: vel.scheduler.reduce_lr_on_plateau
-#      metric_name: val:accuracy
-#      factor: 0.33
-#      patience: 15
-#      cooldown: 20
-#      min_lr: 0.5e-6
-
-  augvis:
-    name: vel.commands.augvis_command
-    samples: 5
diff --git a/examples-configs/classification/cifar10/cifar10_resnetv1_32.yaml b/examples-configs/classification/cifar10/cifar10_resnetv1_32.yaml
deleted file mode 100644
index d85ce5bf..00000000
--- a/examples-configs/classification/cifar10/cifar10_resnetv1_32.yaml
+++ /dev/null
@@ -1,75 +0,0 @@
-name: 'cifar10_resnetv1_32'
-
-
-model:
-  name: vel.models.vision.cifar_resnet_v1
-  img_channels: 3
-  num_classes: 10
-
-  mode: basic # Basic or bottleneck
-  blocks: [5, 5, 5] # ResNet32
-
-source:
-  name: vel.sources.vision.cifar10
-  batch_size: 128
-#  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 200
-    log_frequency: 100
-
-
-    optimizer:
-      name: vel.optimizers.sgd
-      lr: 0.1
-      weight_decay: 0.0001
-      momentum: 0.9
-
-#    optimizer:
-#      name: vel.optimizers.adam
-#      lr: 0.001
-#      weight_decay: 0.001
-
-#    scheduler:
-#      name: vel.scheduler.multi_step
-#      milestones: [20, 50, 100, 150]
-#      gamma: 0.33
-
-    scheduler:
-      name: vel.scheduler.ladder
-      ladder:
-      - [80, 1.0]
-      - [40, 0.1]
-      - [80, 0.01]
-
-#    scheduler:
-#      name: vel.scheduler.reduce_lr_on_plateau
-#      metric_name: val:accuracy
-#      factor: 0.33
-#      patience: 15
-#      cooldown: 20
-#      min_lr: 0.5e-6
-
-  augvis:
-    name: vel.commands.augvis_command
-    samples: 5
-    cases: 3
diff --git a/examples-configs/classification/cifar10/cifar10_resnetv2_110.yaml b/examples-configs/classification/cifar10/cifar10_resnetv2_110.yaml
deleted file mode 100644
index b9998cdf..00000000
--- a/examples-configs/classification/cifar10/cifar10_resnetv2_110.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-name: 'cifar10_resnetv2_110'
-
-
-model:
-  name: vel.models.vision.cifar_resnet_v2
-  img_channels: 3
-  num_classes: 10
-
-  mode: basic # Basic or bottleneck
-  blocks: [18, 18, 18] # ResNet110
-
-source:
-  name: vel.sources.vision.cifar10
-  batch_size: 128
-#  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 200
-    log_frequency: 100
-
-    optimizer:
-      name: vel.optimizers.sgd
-      lr: 0.1
-      weight_decay: 0.0001
-      momentum: 0.9
-
-#    optimizer:
-#      name: vel.optimizers.adam
-#      lr: 0.001
-#      weight_decay: 0.001
-
-#    scheduler:
-#      name: vel.scheduler.multi_step
-#      milestones: [20, 50, 100, 150]
-#      gamma: 0.33
-
-    scheduler:
-      name: vel.scheduler.ladder
-      ladder:
-      - [5, 0.1]  # Special iteration to kickstart convergence
-      - [75, 1.0]
-      - [40, 0.1]
-      - [80, 0.01]
-
-#    scheduler:
-#      name: vel.scheduler.reduce_lr_on_plateau
-#      metric_name: val:accuracy
-#      factor: 0.33
-#      patience: 15
-#      cooldown: 20
-#      min_lr: 0.5e-6
-
-  augvis:
-    name: vel.commands.augvis_command
-    samples: 5
diff --git a/examples-configs/classification/cifar10/cifar10_resnetv2_164_bottleneck.yaml b/examples-configs/classification/cifar10/cifar10_resnetv2_164_bottleneck.yaml
deleted file mode 100644
index fb01e547..00000000
--- a/examples-configs/classification/cifar10/cifar10_resnetv2_164_bottleneck.yaml
+++ /dev/null
@@ -1,73 +0,0 @@
-name: 'cifar10_resnetv2_110'
-
-
-model:
-  name: vel.models.vision.cifar_resnet_v2
-  img_channels: 3
-  num_classes: 10
-
-  inplanes: 64
-  mode: bottleneck # Basic or bottleneck
-  blocks: [18, 18, 18] # ResNet110
-
-source:
-  name: vel.sources.vision.cifar10
-  batch_size: 128
-#  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 200
-    log_frequency: 100
-
-    optimizer:
-      name: vel.optimizers.sgd
-      lr: 0.1
-      weight_decay: 0.0001
-      momentum: 0.9
-
-#    optimizer:
-#      name: vel.optimizers.adam
-#      lr: 0.001
-#      weight_decay: 0.001
-
-#    scheduler:
-#      name: vel.scheduler.multi_step
-#      milestones: [20, 50, 100, 150]
-#      gamma: 0.33
-
-    scheduler:
-      name: vel.scheduler.ladder
-      ladder:
-      - [5, 0.1]  # Special iteration to kickstart convergence
-      - [75, 1.0]
-      - [40, 0.1]
-      - [80, 0.01]
-
-#    scheduler:
-#      name: vel.scheduler.reduce_lr_on_plateau
-#      metric_name: val:accuracy
-#      factor: 0.33
-#      patience: 15
-#      cooldown: 20
-#      min_lr: 0.5e-6
-
-  augvis:
-    name: vel.commands.augvis_command
-    samples: 5
diff --git a/examples-configs/classification/cifar10/cifar10_resnetv2_32.yaml b/examples-configs/classification/cifar10/cifar10_resnetv2_32.yaml
deleted file mode 100644
index 4e0684a4..00000000
--- a/examples-configs/classification/cifar10/cifar10_resnetv2_32.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-name: 'cifar10_resnetv2_110'
-
-
-model:
-  name: vel.models.vision.cifar_resnet_v2
-  img_channels: 3
-  num_classes: 10
-
-  mode: basic # Basic or bottleneck
-  blocks: [5, 5, 5] # ResNet32
-
-source:
-  name: vel.sources.vision.cifar10
-  batch_size: 128
-#  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 200
-    log_frequency: 100
-
-    optimizer:
-      name: vel.optimizers.sgd
-      lr: 0.1
-      weight_decay: 0.0001
-      momentum: 0.9
-
-#    optimizer:
-#      name: vel.optimizers.adam
-#      lr: 0.001
-#      weight_decay: 0.001
-
-#    scheduler:
-#      name: vel.scheduler.multi_step
-#      milestones: [20, 50, 100, 150]
-#      gamma: 0.33
-
-    scheduler:
-      name: vel.scheduler.ladder
-      ladder:
-      - [5, 0.1]  # Special iteration to kickstart convergence
-      - [75, 1.0]
-      - [40, 0.1]
-      - [80, 0.01]
-
-#    scheduler:
-#      name: vel.scheduler.reduce_lr_on_plateau
-#      metric_name: val:accuracy
-#      factor: 0.33
-#      patience: 15
-#      cooldown: 20
-#      min_lr: 0.5e-6
-
-  augvis:
-    name: vel.commands.augvis_command
-    samples: 5
diff --git a/examples-configs/classification/cifar10/cifar10_resnext_29_c1.yaml b/examples-configs/classification/cifar10/cifar10_resnext_29_c1.yaml
deleted file mode 100644
index 88099c4e..00000000
--- a/examples-configs/classification/cifar10/cifar10_resnext_29_c1.yaml
+++ /dev/null
@@ -1,76 +0,0 @@
-name: 'cifar10_resnetv1_32'
-
-
-model:
-  name: vel.models.vision.cifar_resnext
-  img_channels: 3
-  num_classes: 10
-
-  mode: bottleneck # Basic or bottleneck
-  blocks: [3, 3, 3] # ResNext29
-  inplanes: 256
-  image_features: 64
-  divisor: 4
-  cardinality: 1
-
-source:
-  name: vel.sources.vision.cifar10
-  batch_size: 128
-#  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 300
-    log_frequency: 100
-
-
-    optimizer:
-      name: vel.optimizers.sgd
-      lr: 0.1
-      weight_decay: 0.0001
-      momentum: 0.9
-
-#    optimizer:
-#      name: vel.optimizers.adam
-#      lr: 0.001
-#      weight_decay: 0.001
-
-#    scheduler:
-#      name: vel.scheduler.multi_step
-#      milestones: [20, 50, 100, 150]
-#      gamma: 0.33
-
-    scheduler:
-      name: vel.scheduler.ladder
-      ladder:
-      - [150, 1.0]
-      - [75, 0.1]
-      - [75, 0.01]
-
-#    scheduler:
-#      name: vel.scheduler.reduce_lr_on_plateau
-#      metric_name: val:accuracy
-#      factor: 0.33
-#      patience: 15
-#      cooldown: 20
-#      min_lr: 0.5e-6
-
-  augvis:
-    name: vel.commands.augvis_command
-    samples: 5
diff --git a/examples-configs/classification/cifar10/cifar10_resnext_29_c8.yaml b/examples-configs/classification/cifar10/cifar10_resnext_29_c8.yaml
deleted file mode 100644
index 23dcb949..00000000
--- a/examples-configs/classification/cifar10/cifar10_resnext_29_c8.yaml
+++ /dev/null
@@ -1,77 +0,0 @@
-name: 'cifar10_resnetv1_32'
-
-
-model:
-  name: vel.models.vision.cifar_resnext
-  img_channels: 3
-  num_classes: 10
-
-  mode: bottleneck # Basic or bottleneck
-  blocks: [3, 3, 3] # ResNext29
-  inplanes: 256
-  image_features: 64
-  divisor: 4
-  cardinality: 8
-
-source:
-  name: vel.sources.vision.cifar10
-  batch_size: 32
-#  batch_size: 32
-  num_workers: 4
-  normalize: True
-
-  augmentations:
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 32
-    height: 32
-    padding: 4
-    padding_mode: 'reflect'
-
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 300
-    log_frequency: 100
-
-
-    optimizer:
-      name: vel.optimizers.sgd
-      lr: 0.1
-      weight_decay: 0.0001
-      momentum: 0.9
-
-#    optimizer:
-#      name: vel.optimizers.adam
-#      lr: 0.001
-#      weight_decay: 0.001
-
-#    scheduler:
-#      name: vel.scheduler.multi_step
-#      milestones: [20, 50, 100, 150]
-#      gamma: 0.33
-
-    scheduler:
-      name: vel.scheduler.ladder
-      ladder:
-      - [150, 1.0]
-      - [75, 0.1]
-      - [75, 0.01]
-
-#    scheduler:
-#      name: vel.scheduler.reduce_lr_on_plateau
-#      metric_name: val:accuracy
-#      factor: 0.33
-#      patience: 15
-#      cooldown: 20
-#      min_lr: 0.5e-6
-
-  augvis:
-    name: vel.commands.augvis_command
-    samples: 5
diff --git a/examples-configs/classification/imagenet_transfer/cats_vs_dogs_resnet34.yaml b/examples-configs/classification/imagenet_transfer/cats_vs_dogs_resnet34.yaml
deleted file mode 100644
index c10bcd23..00000000
--- a/examples-configs/classification/imagenet_transfer/cats_vs_dogs_resnet34.yaml
+++ /dev/null
@@ -1,124 +0,0 @@
-name: cats_vs_dogs_resnet34
-
-
-model:
-  name: vel.models.imagenet.resnet34
-  fc_layers: [512, 2]
-  dropout: [0.25, 0.5]
-  pretrained: true
-
-
-source:
-  name: vel.sources.img_dir_source
-  # Dataset downloaded from http://files.fast.ai/data/dogscats.zip
-  path: data/dogscats
-  num_workers: 8
-  batch_size: 64
-
-#  tta:
-#    name: vel.augmentations.tta.train_tta
-#    n_augmentations: 4
-
-  augmentations:
-  - name: vel.augmentations.to_array
-    mode: x
-    tags: ["train", "val"]
-
-  - name: vel.augmentations.random_scale
-    mode: x
-    tags: ["train"]
-    size: 224
-    max_zoom: 1.1
-  - name: vel.augmentations.random_rotate
-    mode: x
-    tags: ["train"]
-    deg: 10.0
-  - name: vel.augmentations.random_crop
-    mode: x
-    tags: ["train"]
-    width: 224
-    height: 224
-  - name: vel.augmentations.random_lighting
-    mode: x
-    tags: ["train"]
-    b: 0.05
-    c: 0.05
-  - name: vel.augmentations.random_horizontal_flip
-    mode: x
-    tags: ["train"]
-
-  - name: vel.augmentations.scale_min_size
-    mode: x
-    tags: ["val"]
-    size: 224
-  - name: vel.augmentations.center_crop
-    mode: x
-    tags: ["val"]
-    size: 224
-
-  - name: vel.augmentations.normalize
-    mode: x
-    tags: ["train", "val"]
-    mean: [0.485, 0.456, 0.406]
-    std:  [0.229, 0.224, 0.225]
-
-  - name: vel.augmentations.to_tensor
-    mode: x
-    tags: ["train", "val"]
-
-
-optimizer:
-  name: vel.optimizers.sgd
-  lr: 0.01
-  weight_decay: 0.0
-  momentum: 0.9
-  layer_groups: on
-
-
-commands:
-  train:
-    name: vel.commands.phase_train_command
-    restart: false
-    phases:
-    - name: vel.phase.freeze
-    - name: vel.phase.cycle
-      init_lr: 0.001
-      init_iter: 20
-      max_lr: 0.01
-      min_lr: 0.00
-      interpolate: 'cosine'
-      cycles: 3
-      cycle_len: 1
-    - name: vel.phase.unfreeze
-    - name: vel.phase.cycle
-      init_lr: 0.001
-      init_iter: 20
-
-      max_lr: [1.0e-4, 1.0e-3, 1.0e-2]
-      min_lr: [0.0, 0.0, 0.0]
-      interpolate: 'cosine'
-      cycles: 3
-      cycle_len: 1
-      cycle_mult: 2
-
-  simple_train:
-    name: vel.commands.train_command
-    restart: false
-    epochs: 3
-
-  summary:
-    name: vel.commands.summary_command
-
-  lr_find:
-    name: vel.commands.lr_find_command
-    metric: 'loss'
-    start_lr: 1.0e-5
-    end_lr: 10.0
-    iterations: 500
-    divergence_threshold: 4.0
-    freeze: true
-
-  augvis:
-    name: vel.commands.augvis_command
-    cases: 3
-    samples: 4
diff --git a/examples-configs/classification/mnist/mnist_cnn_01.yaml b/examples-configs/classification/mnist/mnist_cnn_01.yaml
deleted file mode 100644
index 8d3ed19b..00000000
--- a/examples-configs/classification/mnist/mnist_cnn_01.yaml
+++ /dev/null
@@ -1,35 +0,0 @@
-name: 'mnist_cnn_01'
-
-
-model:
-  name: vel.models.vision.mnist_cnn_01
-  img_rows: 28
-  img_cols: 28
-  img_channels: 1
-  num_classes: 10
-
-
-source:
-  name: vel.sources.vision.mnist
-  batch_size: 128
-  normalize: False
-  num_workers: 4
-
-
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    epochs: 12
-    log_frequency: 100
-
-    optimizer:
-      name: vel.optimizers.adadelta
-
-    checkpoint:
-      metric: 'val:loss'
-
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/generative-adversarial/mnist/mnist_gan.yaml b/examples-configs/generative-adversarial/mnist/mnist_gan.yaml
new file mode 100644
index 00000000..5a52deae
--- /dev/null
+++ b/examples-configs/generative-adversarial/mnist/mnist_gan.yaml
@@ -0,0 +1,63 @@
+name: 'mnist_gan'
+
+
+model:
+  name: vel.model.gan.simple_gan
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  latent_dim: 128
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+#  num_workers: 4
+#  pin_memory: true
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    #    - name: vel.data.augmentation.random_scale
+    #      tags: train
+    #      size: 28
+    #      max_zoom: 1.1
+    #    - name: vel.data.augmentation.random_rotate
+    #      tags: train
+    #      deg: 15.0
+    #    - name: vel.data.augmentation.random_crop
+    #      tags: train
+    #      width: 28
+    #      height: 28
+    #      padding: 3
+    #      padding_mode: 'constant'
+    - name: vel.data.transformation.image_to_tensor
+    - name: vel.data.transformation.unsupervised
+
+
+optimizer:
+  name: vel.optimizer.radam
+  lr: 2.0e-4
+  eps: 1.0e-3
+  betas: [0.5, 0.999]
+  max_grad_norm: 5.0
+
+
+#scheduler:
+#  name: vel.scheduler.multi_step
+#  gamma: 0.71968  # 10 * (-1/7)
+#  milestones: [   1,    4,   13,   40,  121,  364, 1093, 3280]
+
+
+commands:
+  augvis:
+    name: vel.command.augvis_command
+    samples: 10
+    cases: 5
+
+  train:
+    name: vel.command.train_command
+    epochs: 400
diff --git a/examples-configs/generative-likelihood/mnist/mnist_cnn_iwae.yaml b/examples-configs/generative-likelihood/mnist/mnist_cnn_iwae.yaml
new file mode 100644
index 00000000..735f2b7a
--- /dev/null
+++ b/examples-configs/generative-likelihood/mnist/mnist_cnn_iwae.yaml
@@ -0,0 +1,53 @@
+name: 'mnist_cnn_iwae'
+
+
+model:
+  name: vel.model.latent.cnn_iwae
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  channels: [64, 128, 256]
+  representation_length: 50
+  analytical_kl_div: true
+  k: 5  # It's hard to sample many samples for this slightly larger network
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+  pin_memory: true
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.binarize_image
+    - name: vel.data.transformation.image_to_tensor
+    - name: vel.data.transformation.unsupervised
+
+
+optimizer:
+  name: vel.optimizer.radam
+  lr: 1.0e-3
+  eps: 1.0e-4
+  max_grad_norm: 10.0
+
+
+scheduler:
+  name: vel.scheduler.multi_step
+  gamma: 0.71968  # 10 * (-1/7)
+  milestones: [   1,    4,   13,   40,  121,  364, 1093, 3280]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 3280
+
+  nll:
+    name: vel.command.latent.vae_nll
+    max_batch: 1_000
+    samples: !param samples = 10
diff --git a/examples-configs/generative-likelihood/mnist/mnist_cnn_vae.yaml b/examples-configs/generative-likelihood/mnist/mnist_cnn_vae.yaml
new file mode 100644
index 00000000..dc3b20b0
--- /dev/null
+++ b/examples-configs/generative-likelihood/mnist/mnist_cnn_vae.yaml
@@ -0,0 +1,65 @@
+name: 'mnist_cnn_vae'
+
+
+model:
+  name: vel.model.latent.cnn_vae
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  channels: [64, 128, 256]
+  representation_length: 50
+  analytical_kl_div: true
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 256
+#  num_workers: 4
+#  pin_memory: true
+
+  transformations:
+    - name: vel.data.transformation.to_array
+#    - name: vel.data.augmentation.random_scale
+#      tags: train
+#      size: 28
+#      max_zoom: 1.1
+#    - name: vel.data.augmentation.random_rotate
+#      tags: train
+#      deg: 15.0
+#    - name: vel.data.augmentation.random_crop
+#      tags: train
+#      width: 28
+#      height: 28
+#      padding: 3
+#      padding_mode: 'constant'
+    - name: vel.data.transformation.binarize_image
+    - name: vel.data.transformation.image_to_tensor
+    - name: vel.data.transformation.unsupervised
+
+
+optimizer:
+  name: vel.optimizer.radam
+  lr: 1.0e-3
+  eps: 1.0e-4
+  max_grad_norm: 10.0
+
+
+scheduler:
+  name: vel.scheduler.multi_step
+  gamma: 0.71968  # 10 * (-1/7)
+  milestones: [   1,    4,   13,   40,  121,  364, 1093, 3280]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 3280
+
+  nll:
+    name: vel.command.latent.vae_nll
+    max_batch: 1_000
+    samples: !param samples = 10
diff --git a/examples-configs/generative-likelihood/mnist/mnist_cnn_vq_vae.yaml b/examples-configs/generative-likelihood/mnist/mnist_cnn_vq_vae.yaml
new file mode 100644
index 00000000..8e1dbed1
--- /dev/null
+++ b/examples-configs/generative-likelihood/mnist/mnist_cnn_vq_vae.yaml
@@ -0,0 +1,54 @@
+name: 'mnist_cnn_vq_vae'
+
+
+model:
+  name: vel.model.latent.vq_vae
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  channels: [64, 128, 256]
+#  channels: [32, 64, 128]
+
+  k: 128
+  d: 64
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+#  num_workers: 4
+#  pin_memory: true
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.image_to_tensor
+    - name: vel.data.transformation.unsupervised
+
+
+optimizer:
+  name: vel.optimizer.radam
+  lr: 1.0e-3
+  eps: 1.0e-4
+  max_grad_norm: 1.0
+
+
+scheduler:
+  name: vel.scheduler.multi_step
+  gamma: 0.71968  # 10 * (-1/7)
+  milestones: [   1,    4,   13,   40,  121,  364, 1093, 3280]
+
+
+commands:
+  augvis:
+    name: vel.command.augvis_command
+    samples: 10
+    cases: 5
+
+  train:
+    name: vel.command.train_command
+#    epochs: 3280
+    epochs: 50
diff --git a/examples-configs/generative-likelihood/mnist/mnist_fc_iwae.yaml b/examples-configs/generative-likelihood/mnist/mnist_fc_iwae.yaml
new file mode 100644
index 00000000..83ea2399
--- /dev/null
+++ b/examples-configs/generative-likelihood/mnist/mnist_fc_iwae.yaml
@@ -0,0 +1,54 @@
+name: 'mnist_fc_iwae'
+
+
+model:
+  name: vel.model.latent.fc_iwae
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  layers: [200, 200]
+  representation_length: 50
+  analytical_kl_div: true
+  k: 50  # Because it's such a small network we can try many importance samples
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+  pin_memory: true
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.binarize_image
+    - name: vel.data.transformation.image_to_tensor
+    - name: vel.data.transformation.unsupervised
+
+
+optimizer:
+  name: vel.optimizer.radam
+  lr: 1.0e-3
+  eps: 1.0e-4
+  max_grad_norm: 10.0
+
+
+
+scheduler:
+  name: vel.scheduler.multi_step
+  gamma: 0.71968  # 10 * (-1/7)
+  milestones: [   1,    4,   13,   40,  121,  364, 1093, 3280]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 3280
+
+  nll:
+    name: vel.command.latent.vae_nll
+    max_batch: 10_000
+    samples: !param samples = 10
diff --git a/examples-configs/generative-likelihood/mnist/mnist_fc_vae.yaml b/examples-configs/generative-likelihood/mnist/mnist_fc_vae.yaml
new file mode 100644
index 00000000..56a3991d
--- /dev/null
+++ b/examples-configs/generative-likelihood/mnist/mnist_fc_vae.yaml
@@ -0,0 +1,52 @@
+name: 'mnist_fc_vae'
+
+
+model:
+  name: vel.model.latent.fc_vae
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  layers: [200, 200]
+  representation_length: 50
+  analytical_kl_div: true
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+  pin_memory: true
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.binarize_image
+    - name: vel.data.transformation.image_to_tensor
+    - name: vel.data.transformation.unsupervised
+
+
+optimizer:
+  name: vel.optimizer.radam
+  lr: 1.0e-3
+  eps: 1.0e-4
+  max_grad_norm: 10.0
+
+
+scheduler:
+  name: vel.scheduler.multi_step
+  gamma: 0.71968  # 10 * (-1/7)
+  milestones: [   1,    4,   13,   40,  121,  364, 1093, 3280]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 3280
+
+  nll:
+    name: vel.command.latent.vae_nll
+    max_batch: 10_000
+    samples: !param samples = 10
\ No newline at end of file
diff --git a/examples-configs/image-classification/cats_vs_dogs/cats_vs_dogs_resnet34.yaml b/examples-configs/image-classification/cats_vs_dogs/cats_vs_dogs_resnet34.yaml
new file mode 100644
index 00000000..83784841
--- /dev/null
+++ b/examples-configs/image-classification/cats_vs_dogs/cats_vs_dogs_resnet34.yaml
@@ -0,0 +1,110 @@
+name: cats_vs_dogs_resnet34
+
+
+model:
+  name: vel.model.imagenet.resnet34
+  fc_layers: [512, 2]
+  dropout: [0.25, 0.5]
+  pretrained: true
+
+
+source:
+  name: vel.data.source.img_dir_source
+  url: http://files.fast.ai/data/dogscats.zip
+  extract_parent: true
+  path: data/dogscats
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  num_workers: 8
+  batch_size: 64
+
+  transformations:
+  - name: vel.data.transformation.to_array
+  - name: vel.data.augmentation.random_scale
+    tags: train
+    size: 224
+    max_zoom: 1.1
+  - name: vel.data.augmentation.random_rotate
+    tags: train
+    deg: 10.0
+  - name: vel.data.augmentation.random_crop
+    tags: train
+    width: 224
+    height: 224
+  - name: vel.data.augmentation.random_lighting
+    tags: train
+    b: 0.05
+    c: 0.05
+  - name: vel.data.augmentation.random_horizontal_flip
+    tags: train
+  - name: vel.data.augmentation.scale_min_size
+    tags: val
+    size: 224
+  - name: vel.data.augmentation.center_crop
+    tags: val
+    size: 224
+
+  - name: vel.data.transformation.normalize
+    tags: ["train", "val"]
+    mean: [0.485, 0.456, 0.406]
+    std:  [0.229, 0.224, 0.225]
+
+  - name: vel.data.transformation.image_to_tensor
+    tags: ["train", "val"]
+
+
+optimizer:
+  name: vel.optimizer.sgd
+  lr: 0.01
+  weight_decay: 0.0
+  momentum: 0.9
+
+
+commands:
+  train:
+    name: vel.command.phase_train_command
+    phases:
+    - name: vel.train.phase.freeze
+      groups: ['top', 'mid']
+    - name: vel.train.phase.cycle
+      init_lr: 0.001
+      init_iter: 20
+      max_lr: 0.01
+      min_lr: 0.00
+      interpolate: 'cosine'
+      cycles: 3
+      cycle_len: 1
+    - name: vel.train.phase.unfreeze
+    - name: vel.train.phase.cycle
+      init_lr: 0.001
+      init_iter: 20
+
+      max_lr: {"top": 1.0e-4, "mid": 1.0e-3, "bottom": 1.0e-2}
+      min_lr: {"top": 0.0, "mid": 0.0, "bottom": 0.0}
+      interpolate: 'cosine'
+      cycles: 3
+      cycle_len: 1
+      cycle_mult: 2
+
+  simple_train:
+    name: vel.command.train_command
+    epochs: 3
+
+  summary:
+    name: vel.command.summary_command
+
+  lr_find:
+    name: vel.command.lr_find_command
+    metric: 'loss'
+    start_lr: 1.0e-5
+    end_lr: 10.0
+    iterations: 500
+    divergence_threshold: 4.0
+    freeze: true
+
+  augvis:
+    name: vel.command.augvis_command
+    cases: 3
+    samples: 4
diff --git a/examples-configs/image-classification/cifar10/cifar10_cnn_01.yaml b/examples-configs/image-classification/cifar10/cifar10_cnn_01.yaml
new file mode 100644
index 00000000..d192c20b
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_cnn_01.yaml
@@ -0,0 +1,47 @@
+name: 'cifar10_cnn_01'
+
+
+model:
+  name: vel.model.vision.cifar10_cnn_01
+  img_rows: 32
+  img_cols: 32
+  img_channels: 3
+  num_classes: 10
+
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: train
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: train
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.adadelta
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 12
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 3
+    cases: 3
diff --git a/examples-configs/image-classification/cifar10/cifar10_resnetv1_110.yaml b/examples-configs/image-classification/cifar10/cifar10_resnetv1_110.yaml
new file mode 100644
index 00000000..c6f45b84
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_resnetv1_110.yaml
@@ -0,0 +1,62 @@
+name: 'cifar10_resnetv1_110'
+
+
+model:
+  name: vel.model.vision.cifar_resnet_v1
+  img_channels: 3
+  num_classes: 10
+
+  mode: basic # Basic or bottleneck
+  blocks: [18, 18, 18] # ResNet110
+
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: ["train"]
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: ["train"]
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizers.sgd
+  lr: 0.1
+  weight_decay: 0.0001
+  momentum: 0.9
+
+
+scheduler:
+  name: vel.scheduler.ladder
+  ladder:
+    - [5, 0.1]  # Special iteration to kickstart convergence
+    - [75, 1.0]
+    - [40, 0.1]
+    - [80, 0.01]
+
+
+commands:
+  train:
+    name: vel.commands.train_command
+    epochs: 200
+    log_frequency: 100
+
+
+  augvis:
+    name: vel.commands.augvis_command
+    samples: 5
+    cases: 3
diff --git a/examples-configs/image-classification/cifar10/cifar10_resnetv1_32.yaml b/examples-configs/image-classification/cifar10/cifar10_resnetv1_32.yaml
new file mode 100644
index 00000000..2cd2fedb
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_resnetv1_32.yaml
@@ -0,0 +1,60 @@
+name: 'cifar10_resnetv1_32'
+
+
+model:
+  name: vel.model.vision.cifar_resnet_v1
+  img_channels: 3
+  num_classes: 10
+
+  mode: basic # Basic or bottleneck
+  blocks: [5, 5, 5] # ResNet32
+
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: ["train"]
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: ["train"]
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.sgd
+  lr: 0.1
+  weight_decay: 0.0001
+  momentum: 0.9
+
+
+scheduler:
+  name: vel.scheduler.ladder
+  ladder:
+    - [80, 1.0]
+    - [40, 0.1]
+    - [80, 0.01]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 200
+
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 5
+    cases: 3
diff --git a/examples-configs/image-classification/cifar10/cifar10_resnetv2_110.yaml b/examples-configs/image-classification/cifar10/cifar10_resnetv2_110.yaml
new file mode 100644
index 00000000..fb0213d6
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_resnetv2_110.yaml
@@ -0,0 +1,60 @@
+name: 'cifar10_resnetv2_110'
+
+
+model:
+  name: vel.model.vision.cifar_resnet_v2
+  img_channels: 3
+  num_classes: 10
+
+  mode: basic # Basic or bottleneck
+  blocks: [18, 18, 18] # ResNet110
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: ["train"]
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: ["train"]
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.sgd
+  lr: 0.1
+  weight_decay: 0.0001
+  momentum: 0.9
+
+
+scheduler:
+  name: vel.scheduler.ladder
+  ladder:
+    - [5, 0.1]  # Special iteration to kickstart convergence
+    - [75, 1.0]
+    - [40, 0.1]
+    - [80, 0.01]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 200
+
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 5
+    cases: 3
diff --git a/examples-configs/image-classification/cifar10/cifar10_resnetv2_164_bottleneck.yaml b/examples-configs/image-classification/cifar10/cifar10_resnetv2_164_bottleneck.yaml
new file mode 100644
index 00000000..a24785bf
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_resnetv2_164_bottleneck.yaml
@@ -0,0 +1,62 @@
+name: 'cifar10_resnetv2_110'
+
+
+model:
+  name: vel.model.vision.cifar_resnet_v2
+  img_channels: 3
+  num_classes: 10
+
+  inplanes: 64
+  mode: bottleneck # Basic or bottleneck
+  blocks: [18, 18, 18] # ResNet110
+
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: ["train"]
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: ["train"]
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.sgd
+  lr: 0.1
+  weight_decay: 0.0001
+  momentum: 0.9
+
+
+scheduler:
+  name: vel.scheduler.ladder
+  ladder:
+    - [5, 0.1]  # Special iteration to kickstart convergence
+    - [75, 1.0]
+    - [40, 0.1]
+    - [80, 0.01]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 200
+
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 5
+    cases: 3
diff --git a/examples-configs/image-classification/cifar10/cifar10_resnetv2_32.yaml b/examples-configs/image-classification/cifar10/cifar10_resnetv2_32.yaml
new file mode 100644
index 00000000..2d67a653
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_resnetv2_32.yaml
@@ -0,0 +1,60 @@
+name: 'cifar10_resnetv2_110'
+
+
+model:
+  name: vel.model.vision.cifar_resnet_v2
+  img_channels: 3
+  num_classes: 10
+
+  mode: basic # Basic or bottleneck
+  blocks: [5, 5, 5] # ResNet32
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: ["train"]
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: ["train"]
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.sgd
+  lr: 0.1
+  weight_decay: 0.0001
+  momentum: 0.9
+
+
+scheduler:
+  name: vel.scheduler.ladder
+  ladder:
+    - [5, 0.1]  # Special iteration to kickstart convergence
+    - [75, 1.0]
+    - [40, 0.1]
+    - [80, 0.01]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 200
+
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 5
+    cases: 3
diff --git a/examples-configs/image-classification/cifar10/cifar10_resnext_29_c1.yaml b/examples-configs/image-classification/cifar10/cifar10_resnext_29_c1.yaml
new file mode 100644
index 00000000..601a9a27
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_resnext_29_c1.yaml
@@ -0,0 +1,64 @@
+name: 'cifar10_resnetv1_32'
+
+
+model:
+  name: vel.model.vision.cifar_resnext
+  img_channels: 3
+  num_classes: 10
+
+  mode: bottleneck # Basic or bottleneck
+  blocks: [3, 3, 3] # ResNext29
+  inplanes: 256
+  image_features: 64
+  divisor: 4
+  cardinality: 1
+
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: ["train"]
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: ["train"]
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.sgd
+  lr: 0.1
+  weight_decay: 0.0001
+  momentum: 0.9
+
+
+scheduler:
+  name: vel.scheduler.ladder
+  ladder:
+    - [150, 1.0]
+    - [75, 0.1]
+    - [75, 0.01]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 300
+
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 5
+    cases: 3
diff --git a/examples-configs/image-classification/cifar10/cifar10_resnext_29_c8.yaml b/examples-configs/image-classification/cifar10/cifar10_resnext_29_c8.yaml
new file mode 100644
index 00000000..632a5f45
--- /dev/null
+++ b/examples-configs/image-classification/cifar10/cifar10_resnext_29_c8.yaml
@@ -0,0 +1,64 @@
+name: 'cifar10_resnetv1_32'
+
+
+model:
+  name: vel.model.vision.cifar_resnext
+  img_channels: 3
+  num_classes: 10
+
+  mode: bottleneck # Basic or bottleneck
+  blocks: [3, 3, 3] # ResNext29
+  inplanes: 256
+  image_features: 64
+  divisor: 4
+  cardinality: 8
+
+
+source:
+  name: vel.data.source.vision.cifar10
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.to_array
+    - name: vel.data.transformation.normalize
+    - name: vel.data.augmentation.random_crop
+      tags: ["train"]
+      width: 32
+      height: 32
+      padding: 4
+      padding_mode: 'reflect'
+    - name: vel.data.augmentation.random_horizontal_flip
+      tags: ["train"]
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.sgd
+  lr: 0.1
+  weight_decay: 0.0001
+  momentum: 0.9
+
+
+scheduler:
+  name: vel.scheduler.ladder
+  ladder:
+    - [150, 1.0]
+    - [75, 0.1]
+    - [75, 0.01]
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 300
+
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 5
+    cases: 3
diff --git a/examples-configs/image-classification/mnist/mnist_cnn_01.yaml b/examples-configs/image-classification/mnist/mnist_cnn_01.yaml
new file mode 100644
index 00000000..f23a96c6
--- /dev/null
+++ b/examples-configs/image-classification/mnist/mnist_cnn_01.yaml
@@ -0,0 +1,39 @@
+name: 'mnist_cnn_01'
+
+
+model:
+  name: vel.model.vision.mnist_cnn_01
+  img_rows: 28
+  img_cols: 28
+  img_channels: 1
+  num_classes: 10
+
+
+source:
+  name: vel.data.source.vision.mnist
+
+
+loader:
+  name: vel.data.loader.dataset_loader
+  batch_size: 128
+#  num_workers: 4
+
+  transformations:
+    - name: vel.data.transformation.image_to_tensor
+
+
+optimizer:
+  name: vel.optimizer.adadelta
+  max_grad_norm: 1.0
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    epochs: 12
+
+
+  augvis:
+    name: vel.command.augvis_command
+    samples: 3
+    cases: 3
diff --git a/examples-configs/language-modeling/shakespeare/gen_shakespeare.yaml b/examples-configs/language-modeling/shakespeare/gen_shakespeare.yaml
new file mode 100644
index 00000000..f27a7161
--- /dev/null
+++ b/examples-configs/language-modeling/shakespeare/gen_shakespeare.yaml
@@ -0,0 +1,62 @@
+name: 'gen_shakespeare'
+
+
+source:
+  name: vel.data.source.nlp.text_url
+  # Andrej Karpathy built a small (4.4mb) file with combined all works of Shakespeare
+  url: 'https://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt'
+  local_dir: './rnn_shakespeare'
+
+
+loader:
+  name: vel.data.loader.text_character_loader
+  sequence_length: 128
+  batch_size: 64
+
+
+model:
+  name: vel.model.nlp.language_model
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.nlp.alphabet_embedding
+        dim: 512
+      - name: vel.net.layer.rnn
+        hidden_size: 512
+        rnn_type: 'lstm'
+      - name: vel.net.layer.dropout
+        p: 0.3
+      - name: vel.net.layer.rnn
+        hidden_size: 512
+        rnn_type: 'lstm'
+        dropout: 0.5
+      - name: vel.net.layer.dropout
+        p: 0.3
+      - name: vel.net.layer.rnn
+        hidden_size: 512
+        rnn_type: 'lstm'
+        dropout: 0.5
+      - name: vel.net.layer.dropout
+        p: 0.3
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 1.0e-3
+  epsilon: 1.0e-5
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    max_grad_norm: 0.5
+    epochs: 20
+
+  generate:
+    name: vel.command.rnn.generate_text
+    start_letter: !param start_letter = 'A'
+    length: !param length = 500
+    temperature: !param temperature = 0.8
+
+
diff --git a/examples-configs/nlp/classification/imdb_sentiment_gru.yaml b/examples-configs/nlp/classification/imdb_sentiment_gru.yaml
deleted file mode 100644
index 1c486db6..00000000
--- a/examples-configs/nlp/classification/imdb_sentiment_gru.yaml
+++ /dev/null
@@ -1,46 +0,0 @@
-name: 'imdb_sentiment_gru'
-
-
-source:
-  name: vel.sources.nlp.imdb
-  vectors: "glove.6B.100d"  # precomputed 100-dimensional embeddings
-  batch_size: 32
-
-
-model:
-  name: vel.models.rnn.multilayer_rnn_sequence_classification
-
-  input_block:
-    name: vel.modules.input.embedding
-    alphabet_size: 25_002  # Size of the alphabet
-
-    output_dim: 100 # Embedding dimension
-
-    pretrained: True
-#    frozen: True
-
-  rnn_type: 'gru'
-  rnn_layers: [256, 128]
-  rnn_dropout: 0.5
-  bidirectional: True
-
-  linear_layers: [64]
-  linear_dropout: 0.2
-
-  output_dim: 2 # Positive or negative sentiment
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: [1.0e-4, 1.0e-3, 1.0e-2, 1.0e-2]
-  weight_decay: [0.0, 0.0001, 0.001, 0.001]
-  epsilon: 1.0e-5
-  betas: [0.7, 0.99]
-  layer_groups: true
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    max_grad_norm: 5.0
-    epochs: 10
diff --git a/examples-configs/nlp/generation/gen_shakespeare_gru.yaml b/examples-configs/nlp/generation/gen_shakespeare_gru.yaml
deleted file mode 100644
index 4e54fb31..00000000
--- a/examples-configs/nlp/generation/gen_shakespeare_gru.yaml
+++ /dev/null
@@ -1,46 +0,0 @@
-name: 'gen_shakespeare_gru'
-
-
-source:
-  name: vel.sources.nlp.text_url
-  # Andrej Karpathy built a small (4.4mb) file with combined all works of Shakespeare
-  url: 'https://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt'
-  local_dir: './rnn_shakespeare'
-  sequence_length: 128
-  batch_size: 64
-
-
-model:
-  name: vel.models.rnn.multilayer_rnn_sequence_model
-
-  input_block:
-    name: vel.modules.input.one_hot_encoding
-    alphabet_size: 68  # Size of the alphabet + 1
-
-  hidden_layers: [512, 512, 512]
-  output_dim: 68 # Size of the alphabet + 1
-  dropout: 0.5
-  rnn_type: 'gru'
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 1.0e-3
-  epsilon: 1.0e-5
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    max_grad_norm: 0.5
-    epochs: 20
-
-  generate:
-    name: vel.commands.rnn.generate_text
-    start_letter: !param start_letter = 'A'
-    length: !param length = 500
-    temperature: !param temperature = 0.8
-
-
-
-
diff --git a/examples-configs/nlp/generation/gen_shakespeare_gru_embedding.yaml b/examples-configs/nlp/generation/gen_shakespeare_gru_embedding.yaml
deleted file mode 100644
index 9603cb8d..00000000
--- a/examples-configs/nlp/generation/gen_shakespeare_gru_embedding.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-name: 'gen_shakespeare_gru_embedding'
-
-
-source:
-  name: vel.sources.nlp.text_url
-  # Andrej Karpathy built a small (4.4mb) file with combined all works of Shakespeare
-  url: 'https://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt'
-  local_dir: './rnn_shakespeare'
-  sequence_length: 128
-  batch_size: 64
-
-
-model:
-  name: vel.models.rnn.multilayer_rnn_sequence_model
-
-  input_block:
-    name: vel.modules.input.embedding
-    alphabet_size: 68  # Size of the alphabet + 1
-    output_dim: 512 # Embedding dimension
-
-  hidden_layers: [512, 512, 512]
-  output_dim: 68 # Size of the alphabet + 1
-  dropout: 0.5
-  rnn_type: 'gru'
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 1.0e-3
-  epsilon: 1.0e-5
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    max_grad_norm: 0.5
-    epochs: 20
-
-  generate:
-    name: vel.commands.rnn.generate_text
-    start_letter: !param start_letter = 'A'
-    length: !param length = 500
-    temperature: !param temperature = 0.8
-
-
diff --git a/examples-configs/nlp/generation/gen_shakespeare_lstm.yaml b/examples-configs/nlp/generation/gen_shakespeare_lstm.yaml
deleted file mode 100644
index 56129c6f..00000000
--- a/examples-configs/nlp/generation/gen_shakespeare_lstm.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-name: 'gen_shakespeare_lstm'
-
-
-source:
-  name: vel.sources.nlp.text_url
-  # Andrej Karpathy built a small (4.4mb) file with combined all works of Shakespeare
-  url: 'https://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt'
-  local_dir: './rnn_shakespeare'
-  sequence_length: 128
-  batch_size: 64
-
-
-model:
-  name: vel.models.rnn.multilayer_rnn_sequence_model
-
-  input_block:
-    name: vel.modules.input.one_hot_encoding
-    alphabet_size: 68  # Size of the alphabet + 1
-
-  hidden_layers: [512, 512, 512]
-  output_dim: 68 # Size of the alphabet + 1
-  dropout: 0.5
-  rnn_type: 'lstm'
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 1.0e-3
-  epsilon: 1.0e-5
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    max_grad_norm: 0.5
-    epochs: 20
-
-  generate:
-    name: vel.commands.rnn.generate_text
-    start_letter: !param start_letter = 'A'
-    length: !param length = 500
-    temperature: !param temperature = 0.8
-
-
-
diff --git a/examples-configs/nlp/generation/gen_shakespeare_lstm_embedding.yaml b/examples-configs/nlp/generation/gen_shakespeare_lstm_embedding.yaml
deleted file mode 100644
index 79a91d57..00000000
--- a/examples-configs/nlp/generation/gen_shakespeare_lstm_embedding.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-name: 'gen_shakespeare_lstm_embedding'
-
-
-source:
-  name: vel.sources.nlp.text_url
-  # Andrej Karpathy built a small (4.4mb) file with combined all works of Shakespeare
-  url: 'https://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt'
-  local_dir: './rnn_shakespeare'
-  sequence_length: 128
-  batch_size: 64
-
-
-model:
-  name: vel.models.rnn.multilayer_rnn_sequence_model
-
-  input_block:
-    name: vel.modules.input.embedding
-    alphabet_size: 68  # Size of the alphabet + 1
-    output_dim: 512 # Embedding dimension
-
-  hidden_layers: [512, 512, 512]
-  output_dim: 68 # Size of the alphabet + 1
-  dropout: 0.5
-  rnn_type: 'lstm'
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 1.0e-3
-  epsilon: 1.0e-5
-
-
-commands:
-  train:
-    name: vel.commands.train_command
-    max_grad_norm: 0.5
-    epochs: 20
-
-  generate:
-    name: vel.commands.rnn.generate_text
-    start_letter: !param start_letter = 'A'
-    length: !param length = 500
-    temperature: !param temperature = 0.8
-
-
diff --git a/examples-configs/rl/atari/a2c/airraid_a2c.yaml b/examples-configs/rl/atari/a2c/airraid_a2c.yaml
deleted file mode 100644
index 6869f6ad..00000000
--- a/examples-configs/rl/atari/a2c/airraid_a2c.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'airraid_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'AirRaidNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'airraid_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/breakout_a2c.yaml b/examples-configs/rl/atari/a2c/breakout_a2c.yaml
deleted file mode 100644
index 77b7593b..00000000
--- a/examples-configs/rl/atari/a2c/breakout_a2c.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'breakout_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/breakout_a2c_lstm.yaml b/examples-configs/rl/atari/a2c/breakout_a2c_lstm.yaml
deleted file mode 100644
index 8593be1c..00000000
--- a/examples-configs/rl/atari/a2c/breakout_a2c_lstm.yaml
+++ /dev/null
@@ -1,72 +0,0 @@
-name: 'breakout_a2c_lstm'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 1  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_rnn_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn_rnn
-    input_width: 84
-    input_height: 84
-    input_channels: 1  # The same as frame_history
-    rnn_type: 'lstm'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-  shuffle_transitions: off  # Required for RNN policies
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/breakout_a2c_tf_rmsprop.yaml b/examples-configs/rl/atari/a2c/breakout_a2c_tf_rmsprop.yaml
deleted file mode 100644
index a3acb3f2..00000000
--- a/examples-configs/rl/atari/a2c/breakout_a2c_tf_rmsprop.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'breakout_a2c_tf_rmsprop'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop_tf
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-6
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/freeway_a2c.yaml b/examples-configs/rl/atari/a2c/freeway_a2c.yaml
deleted file mode 100644
index d8f7ce2c..00000000
--- a/examples-configs/rl/atari/a2c/freeway_a2c.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'freeway_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'FreewayNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'freeway_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/pacman_a2c.yaml b/examples-configs/rl/atari/a2c/pacman_a2c.yaml
deleted file mode 100644
index 3ad255b1..00000000
--- a/examples-configs/rl/atari/a2c/pacman_a2c.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'pacman_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'MsPacmanNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'pacman_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/pitfall_a2c.yaml b/examples-configs/rl/atari/a2c/pitfall_a2c.yaml
deleted file mode 100644
index fa9b467e..00000000
--- a/examples-configs/rl/atari/a2c/pitfall_a2c.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'pitfall_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'PitfallNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'pitfall_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/pong_a2c.yaml b/examples-configs/rl/atari/a2c/pong_a2c.yaml
deleted file mode 100644
index 8b15fb6b..00000000
--- a/examples-configs/rl/atari/a2c/pong_a2c.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'pong_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'PongNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'pong_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/pong_a2c_lstm.yaml b/examples-configs/rl/atari/a2c/pong_a2c_lstm.yaml
deleted file mode 100644
index e7023fd6..00000000
--- a/examples-configs/rl/atari/a2c/pong_a2c_lstm.yaml
+++ /dev/null
@@ -1,71 +0,0 @@
-name: 'pong_a2c_lstm'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'PongNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_rnn_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn_rnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-  shuffle_transitions: off  # Required for RNN policies
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'pong_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/qbert_a2c.yaml b/examples-configs/rl/atari/a2c/qbert_a2c.yaml
deleted file mode 100644
index 32c97af6..00000000
--- a/examples-configs/rl/atari/a2c/qbert_a2c.yaml
+++ /dev/null
@@ -1,70 +0,0 @@
-name: 'qbert_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'QbertNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-    rnn_type: 'lstm'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'qbert_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/a2c/space_invaders_a2c.yaml b/examples-configs/rl/atari/a2c/space_invaders_a2c.yaml
deleted file mode 100644
index 6333f1d2..00000000
--- a/examples-configs/rl/atari/a2c/space_invaders_a2c.yaml
+++ /dev/null
@@ -1,69 +0,0 @@
-name: 'space_invaders_a2c'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'SpaceInvadersNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-    discount_factor: 0.99
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  number_of_steps: 5 # How many environment steps go into a single batch
-  parallel_envs: 16 # How many environments to run in parallel
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 100
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'space_invaders_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  visdom:
-    name: vel.commands.vis_store_command
diff --git a/examples-configs/rl/atari/acer/beam_rider_acer_trust_region.yaml b/examples-configs/rl/atari/acer/beam_rider_acer_trust_region.yaml
deleted file mode 100644
index 3758d9ea..00000000
--- a/examples-configs/rl/atari/acer/beam_rider_acer_trust_region.yaml
+++ /dev/null
@@ -1,83 +0,0 @@
-name: 'beamrider_acer'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BeamRiderNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.trajectory_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 1_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 50_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-  algo:
-    name: vel.rl.algo.policy_gradient.acer
-    entropy_coefficient: 0.01
-    q_coefficient: 0.5
-    rho_cap: 10.0
-    retrace_rho_cap: 1.0
-
-    max_grad_norm: 10.0
-    discount_factor: 0.99
-
-    trust_region: true
-    trust_region_delta: 1.0
-
-  parallel_envs: 12 # How many environments to run in parallel
-  number_of_steps: 20 # How many environment steps go into a single batch
-  experience_replay: 4
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  #  epsilon: 1.0e-5
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 10
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'beamrider_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    parallel_envs: 12 # How many environments to run in parallel
diff --git a/examples-configs/rl/atari/acer/breakout_acer.yaml b/examples-configs/rl/atari/acer/breakout_acer.yaml
deleted file mode 100644
index 5d4d787e..00000000
--- a/examples-configs/rl/atari/acer/breakout_acer.yaml
+++ /dev/null
@@ -1,82 +0,0 @@
-name: 'breakout_acer_notr'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.trajectory_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 1_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 50_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-  algo:
-    name: vel.rl.algo.policy_gradient.acer
-    entropy_coefficient: 0.01
-    q_coefficient: 0.5
-    rho_cap: 10.0
-    retrace_rho_cap: 1.0
-
-    max_grad_norm: 10.0
-    discount_factor: 0.99
-
-    trust_region: false
-
-  parallel_envs: 12 # How many environments to run in parallel
-  number_of_steps: 20 # How many environment steps go into a single batch
-  experience_replay: 4
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-#  epsilon: 1.0e-5
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 30
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    parallel_envs: 12 # How many environments to run in parallel
diff --git a/examples-configs/rl/atari/acer/breakout_acer_trust_region.yaml b/examples-configs/rl/atari/acer/breakout_acer_trust_region.yaml
deleted file mode 100644
index 7fc824ab..00000000
--- a/examples-configs/rl/atari/acer/breakout_acer_trust_region.yaml
+++ /dev/null
@@ -1,83 +0,0 @@
-name: 'breakout_acer_trust_region'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.trajectory_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 1_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 50_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-  algo:
-    name: vel.rl.algo.policy_gradient.acer
-    entropy_coefficient: 0.01
-    q_coefficient: 0.5
-    rho_cap: 10.0
-    retrace_rho_cap: 1.0
-
-    max_grad_norm: 10.0
-    discount_factor: 0.99
-
-    trust_region: true
-    trust_region_delta: 1.0
-
-  parallel_envs: 12 # How many environments to run in parallel
-  number_of_steps: 20 # How many environment steps go into a single batch
-  experience_replay: 4
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  #  epsilon: 1.0e-5
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 30
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    parallel_envs: 12 # How many environments to run in parallel
diff --git a/examples-configs/rl/atari/acer/seaquest_acer_trust_region.yaml b/examples-configs/rl/atari/acer/seaquest_acer_trust_region.yaml
deleted file mode 100644
index 4fab32f0..00000000
--- a/examples-configs/rl/atari/acer/seaquest_acer_trust_region.yaml
+++ /dev/null
@@ -1,83 +0,0 @@
-name: 'seaquest_acer'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'SeaquestNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.trajectory_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 1_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 50_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-  algo:
-    name: vel.rl.algo.policy_gradient.acer
-    entropy_coefficient: 0.01
-    q_coefficient: 0.5
-    rho_cap: 10.0
-    retrace_rho_cap: 1.0
-
-    max_grad_norm: 10.0
-    discount_factor: 0.99
-
-    trust_region: true
-    trust_region_delta: 1.0
-
-  parallel_envs: 12 # How many environments to run in parallel
-  number_of_steps: 20 # How many environment steps go into a single batch
-  experience_replay: 4
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  #  epsilon: 1.0e-5
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 10
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'seaquest_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    parallel_envs: 12 # How many environments to run in parallel
diff --git a/examples-configs/rl/atari/acer/space_invaders_acer.yaml b/examples-configs/rl/atari/acer/space_invaders_acer.yaml
deleted file mode 100644
index b1dd8001..00000000
--- a/examples-configs/rl/atari/acer/space_invaders_acer.yaml
+++ /dev/null
@@ -1,82 +0,0 @@
-name: 'spaceinvaders_acer_notr'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'SpaceInvadersNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.trajectory_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 1_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 50_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-  algo:
-    name: vel.rl.algo.policy_gradient.acer
-    entropy_coefficient: 0.01
-    q_coefficient: 0.5
-    rho_cap: 10.0
-    retrace_rho_cap: 1.0
-
-    max_grad_norm: 10.0
-    discount_factor: 0.99
-
-    trust_region: false
-
-  parallel_envs: 12 # How many environments to run in parallel
-  number_of_steps: 20 # How many environment steps go into a single batch
-  experience_replay: 4
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  #  epsilon: 1.0e-5
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 30
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'spaceinvaders_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    parallel_envs: 12 # How many environments to run in parallel
diff --git a/examples-configs/rl/atari/acer/space_invaders_acer_trust_region.yaml b/examples-configs/rl/atari/acer/space_invaders_acer_trust_region.yaml
deleted file mode 100644
index 0564d229..00000000
--- a/examples-configs/rl/atari/acer/space_invaders_acer_trust_region.yaml
+++ /dev/null
@@ -1,83 +0,0 @@
-name: 'spaceinvaders_acer'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'SpaceInvadersNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.trajectory_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 1_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 50_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-  algo:
-    name: vel.rl.algo.policy_gradient.acer
-    entropy_coefficient: 0.01
-    q_coefficient: 0.5
-    rho_cap: 10.0
-    retrace_rho_cap: 1.0
-
-    max_grad_norm: 10.0
-    discount_factor: 0.99
-
-    trust_region: true
-    trust_region_delta: 1.0
-
-  parallel_envs: 12 # How many environments to run in parallel
-  number_of_steps: 20 # How many environment steps go into a single batch
-  experience_replay: 4
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 7.0e-4
-  alpha: 0.99
-  #  epsilon: 1.0e-5
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 10
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'spaceinvaders_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    parallel_envs: 12 # How many environments to run in parallel
diff --git a/examples-configs/rl/atari/atari_a2c.yaml b/examples-configs/rl/atari/atari_a2c.yaml
new file mode 100644
index 00000000..f4e6e76f
--- /dev/null
+++ b/examples-configs/rl/atari/atari_a2c.yaml
@@ -0,0 +1,67 @@
+name: 'atari_a2c'
+
+
+env:
+  name: vel.rl.env.classic_atari
+  game: !param game = 'BreakoutNoFrameskip-v4'
+
+
+vec_env:
+  name: vel.rl.vecenv.shared_mem
+  frame_history: 4  # How many stacked frames go into a single observation
+
+
+model:
+  name: vel.rl.policy.a2c
+
+  entropy_coefficient: 0.01
+  value_coefficient: 0.5
+  discount_factor: 0.99
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, for action and value heads
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  number_of_steps: 5 # How many environment steps go into a single batch
+  parallel_envs: 16 # How many environments to run in parallel
+  batch_size: 256 # How many samples can go into the model once
+
+
+optimizer:
+  name: vel.optimizer.rmsprop
+  lr: 7.0e-4
+  alpha: 0.99
+  epsilon: 1.0e-3
+  max_grad_norm: 0.5
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.1e7
+    batches_per_epoch: 100
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'atari_vid_{:04}.avi'
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    parallel_envs: 16 # How many environments to run in parallel
+
+    takes: 20
+
+  visdom:
+    name: vel.command.vis_store_command
diff --git a/examples-configs/rl/atari/atari_a2c_lstm.yaml b/examples-configs/rl/atari/atari_a2c_lstm.yaml
new file mode 100644
index 00000000..25476fed
--- /dev/null
+++ b/examples-configs/rl/atari/atari_a2c_lstm.yaml
@@ -0,0 +1,71 @@
+name: 'atari_a2c_lstm'
+
+
+env:
+  name: vel.rl.env.classic_atari
+  game: !param game = 'BreakoutNoFrameskip-v4'
+
+
+vec_env:
+  name: vel.rl.vecenv.shared_mem
+  frame_history: 1  # How many stacked frames go into a single observation
+
+
+model:
+  name: vel.rl.policy.a2c_rnn
+
+  entropy_coefficient: 0.01
+  value_coefficient: 0.5
+  discount_factor: 0.99
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn
+      - name: vel.rl.layer.rnn_cell
+        hidden_size: 512
+        rnn_type: 'lstm'
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, for action and value heads
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  number_of_steps: 5 # How many environment steps go into a single batch
+  parallel_envs: 16 # How many environments to run in parallel
+
+  shuffle_transitions: off  # Required for RNN policies
+
+
+optimizer:
+  name: vel.optimizer.rmsprop
+  lr: 7.0e-4
+  alpha: 0.99
+  epsilon: 1.0e-3
+  max_grad_norm: 0.5
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.1e7
+    batches_per_epoch: 100
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'atari_vid_{:04}.avi'
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    parallel_envs: 16 # How many environments to run in parallel
+
+    takes: 20
+
+  visdom:
+    name: vel.command.vis_store_command
diff --git a/examples-configs/rl/atari/atari_a2c_tf_rmsprop.yaml b/examples-configs/rl/atari/atari_a2c_tf_rmsprop.yaml
new file mode 100644
index 00000000..0b013bed
--- /dev/null
+++ b/examples-configs/rl/atari/atari_a2c_tf_rmsprop.yaml
@@ -0,0 +1,66 @@
+name: 'atari_a2c_tf_rmsprop'
+
+
+env:
+  name: vel.rl.env.classic_atari
+  game: !param game = 'BreakoutNoFrameskip-v4'
+
+
+vec_env:
+  name: vel.rl.vecenv.shared_mem
+  frame_history: 4  # How many stacked frames go into a single observation
+
+
+model:
+  name: vel.rl.policy.a2c
+
+  entropy_coefficient: 0.01
+  value_coefficient: 0.5
+  discount_factor: 0.99
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, for action and value heads
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  number_of_steps: 5 # How many environment steps go into a single batch
+  parallel_envs: 16 # How many environments to run in parallel
+  batch_size: 256 # How many samples can go into the model once
+
+
+optimizer:
+  name: vel.optimizer.rmsprop_tf
+  lr: 7.0e-4
+  alpha: 0.99
+  epsilon: 1.0e-6
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.1e7
+    batches_per_epoch: 100
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'atari_vid_{:04}.avi'
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    parallel_envs: 16 # How many environments to run in parallel
+
+    takes: 20
+
+  visdom:
+    name: vel.command.vis_store_command
diff --git a/examples-configs/rl/atari/acer/beam_rider_acer.yaml b/examples-configs/rl/atari/atari_acer.yaml
similarity index 58%
rename from examples-configs/rl/atari/acer/beam_rider_acer.yaml
rename to examples-configs/rl/atari/atari_acer.yaml
index 123c3c0b..cde689a8 100644
--- a/examples-configs/rl/atari/acer/beam_rider_acer.yaml
+++ b/examples-configs/rl/atari/atari_acer.yaml
@@ -1,37 +1,47 @@
-name: 'beamrider_acer_notr'
+name: 'atari_acer'
 
 
 env:
   name: vel.rl.env.classic_atari
-  game: 'BeamRiderNoFrameskip-v4'
+  game: !param game = 'BreakoutNoFrameskip-v4'
 
 
 vec_env:
   name: vel.rl.vecenv.shared_mem
   frame_history: 4  # How many stacked frames go into a single observation
 
+  algo:
+    name: vel.rl.algo.policy_gradient.acer
 
 model:
-  name: vel.rl.models.q_stochastic_policy_model
+  name: vel.rl.policy.acer
+
+  entropy_coefficient: 0.01
+  q_coefficient: 0.5
+  rho_cap: 10.0
+  retrace_rho_cap: 1.0
+
+  discount_factor: 0.99
 
-  input_block:
-    name: vel.modules.input.image_to_tensor
+  trust_region: true
 
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, for action and value heads
 
 
 reinforcer:
-  name: vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer
+  name: vel.rl.reinforcer.buffered_mixed_policy_iteration_reinforcer
 
   env_roller:
     name: vel.rl.env_roller.trajectory_replay_env_roller
 
     replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
+      name: vel.rl.buffer.circular_replay_buffer
 
       buffer_initial_size: 1_000 # How many samples we need in the buffer before we start using replay buffer
       buffer_capacity: 50_000
@@ -40,17 +50,6 @@ reinforcer:
       frame_stack_compensation: true
       frame_history: 4  # How many stacked frames go into a single observation
 
-  algo:
-    name: vel.rl.algo.policy_gradient.acer
-    entropy_coefficient: 0.01
-    q_coefficient: 0.5
-    rho_cap: 10.0
-    retrace_rho_cap: 1.0
-
-    max_grad_norm: 10.0
-    discount_factor: 0.99
-
-    trust_region: false
 
   parallel_envs: 12 # How many environments to run in parallel
   number_of_steps: 20 # How many environment steps go into a single batch
@@ -58,25 +57,26 @@ reinforcer:
 
 
 optimizer:
-  name: vel.optimizers.rmsprop
+  name: vel.optimizer.rmsprop
   lr: 7.0e-4
   alpha: 0.99
   #  epsilon: 1.0e-5
   epsilon: 1.0e-3
+  max_grad_norm: 10.0
 
 
 commands:
   train:
-    name: vel.rl.commands.rl_train_command
+    name: vel.rl.command.rl_train_command
     total_frames: 1.1e7
     batches_per_epoch: 30
 
   record:
-    name: vel.rl.commands.record_movie_command
+    name: vel.rl.command.record_movie_command
     takes: 10
-    videoname: 'beamrider_vid_{:04}.avi'
+    videoname: 'atari_vid_{:04}.avi'
 
   evaluate:
-    name: vel.rl.commands.evaluate_env_command
+    name: vel.rl.command.evaluate_env_command
     takes: 100
     parallel_envs: 12 # How many environments to run in parallel
diff --git a/examples-configs/rl/atari/dqn/breakout_dueling_ddqn_prioritized.yaml b/examples-configs/rl/atari/atari_ddqn.yaml
similarity index 53%
rename from examples-configs/rl/atari/dqn/breakout_dueling_ddqn_prioritized.yaml
rename to examples-configs/rl/atari/atari_ddqn.yaml
index 017a0c6e..9decd793 100644
--- a/examples-configs/rl/atari/dqn/breakout_dueling_ddqn_prioritized.yaml
+++ b/examples-configs/rl/atari/atari_ddqn.yaml
@@ -1,9 +1,9 @@
-name: 'breakout_dueling_ddqn_prioritized'
+name: 'atari_dueling_ddqn_prioritized'
 
 
 env:
   name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
+  game: !param game = 'BreakoutNoFrameskip-v4'
 
 
 vec_env:
@@ -12,26 +12,35 @@ vec_env:
 
 
 model:
-  name: vel.rl.models.q_dueling_model
+  name: vel.rl.policy.dqn
 
-  input_block:
-    name: vel.modules.input.image_to_tensor
+  double_dqn: true
+  dueling_dqn: true
+  target_update_frequency: 10_000  # After how many batches to update the target network
 
-  backbone:
-    name: vel.rl.models.backbone.double_nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
+  discount_factor: 0.99
+
+  epsilon:
+    name: vel.function.linear_and_constant
+    end_of_interpolation: 0.1
+    initial_value: 1.0
+    final_value: 0.1
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.double_nature_cnn
 
 
 reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
+  name: vel.rl.reinforcer.buffered_off_policy_iteration_reinforcer
 
   env_roller:
     name: vel.rl.env_roller.transition_replay_env_roller
 
     replay_buffer:
-      name: vel.rl.buffers.prioritized_circular_replay_buffer
+      name: vel.rl.buffer.prioritized_circular_replay_buffer
 
       buffer_initial_size: 30_000 # How many samples we need in the buffer before we start using replay buffer
       buffer_capacity: 250_000
@@ -42,30 +51,12 @@ reinforcer:
 
       priority_exponent: 0.6
       priority_weight:
-        name: vel.schedules.linear
+        name: vel.function.linear
         initial_value: 0.4
         final_value: 1.0
 
       priority_epsilon: 1.0e-6
 
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    double_dqn: true
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
   rollout_steps: 4 # How many environment steps to perform per batch of training
   training_steps: 32 # How many environment steps (per env) to perform per training round
 
@@ -73,24 +64,25 @@ reinforcer:
 
 
 optimizer:
-  name: vel.optimizers.rmsprop
+  name: vel.optimizer.rmsprop
   lr: 2.5e-4
   alpha: 0.95
   momentum: 0.95
   epsilon: 1.0e-1
+  max_grad_norm: 0.5
 
 
 commands:
   train:
-    name: vel.rl.commands.rl_train_command
+    name: vel.rl.command.rl_train_command
     total_frames: 1.1e7  # 11M
     batches_per_epoch: 2500
 
   record:
-    name: vel.rl.commands.record_movie_command
+    name: vel.rl.command.record_movie_command
     takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
+    videoname: 'atari_vid_{:04}.avi'
 
   evaluate:
-    name: vel.rl.commands.evaluate_env_command
+    name: vel.rl.command.evaluate_env_command
     takes: 100
diff --git a/examples-configs/rl/atari/dqn/breakout_ddqn.yaml b/examples-configs/rl/atari/atari_dqn.yaml
similarity index 54%
rename from examples-configs/rl/atari/dqn/breakout_ddqn.yaml
rename to examples-configs/rl/atari/atari_dqn.yaml
index fb6fb2bf..2406ea1e 100644
--- a/examples-configs/rl/atari/dqn/breakout_ddqn.yaml
+++ b/examples-configs/rl/atari/atari_dqn.yaml
@@ -1,9 +1,9 @@
-name: 'breakout_ddqn'
+name: 'atari_dqn'
 
 
 env:
   name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
+  game: !param game = 'BreakoutNoFrameskip-v4'
 
 
 vec_env:
@@ -12,26 +12,32 @@ vec_env:
 
 
 model:
-  name: vel.rl.models.q_model
+  name: vel.rl.policy.dqn
 
-  input_block:
-    name: vel.modules.input.image_to_tensor
+  target_update_frequency: 10_000  # After how many batches to update the target network
+  discount_factor: 0.99
 
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
+  epsilon:
+    name: vel.function.linear_and_constant
+    end_of_interpolation: 0.1
+    initial_value: 1.0
+    final_value: 0.1
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn
 
 
 reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
+  name: vel.rl.reinforcer.buffered_off_policy_iteration_reinforcer
 
   env_roller:
     name: vel.rl.env_roller.transition_replay_env_roller
 
     replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
+      name: vel.rl.buffer.circular_replay_buffer
 
       buffer_initial_size: 30_000 # How many samples we need in the buffer before we start using replay buffer
       buffer_capacity: 250_000
@@ -40,39 +46,32 @@ reinforcer:
       frame_stack_compensation: true
       frame_history: 4  # How many stacked frames go into a single observation
 
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    double_dqn: true
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
   rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
   training_steps: 32 # How many environment steps (per env) to perform per training round
   parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
 
 
 optimizer:
-  name: vel.optimizers.rmsprop
+  name: vel.optimizer.rmsprop
   lr: 2.5e-4
   alpha: 0.95
   momentum: 0.95
   epsilon: 1.0e-1
+  max_grad_norm: 0.5
 
 
 commands:
   train:
-    name: vel.rl.commands.rl_train_command
+    name: vel.rl.command.rl_train_command
     total_frames: 1.1e7  # 11M
     batches_per_epoch: 2500
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'atari_vid_{:04}.avi'
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    takes: 100
+    parallel_envs: 1
diff --git a/examples-configs/rl/atari/atari_ppo.yaml b/examples-configs/rl/atari/atari_ppo.yaml
new file mode 100644
index 00000000..617b8a1f
--- /dev/null
+++ b/examples-configs/rl/atari/atari_ppo.yaml
@@ -0,0 +1,79 @@
+name: 'atari_ppo'
+
+
+env:
+  name: vel.rl.env.classic_atari
+  game: !param game = 'BreakoutNoFrameskip-v4'
+
+
+vec_env:
+  name: vel.rl.vecenv.shared_mem
+  frame_history: 4  # How many stacked frames go into a single observation
+
+
+model:
+  name: vel.rl.policy.ppo
+
+  cliprange:
+    name: vel.function.linear
+    initial_value: 0.1
+    final_value: 0.0
+
+  entropy_coefficient: 0.01
+  value_coefficient: 0.5
+
+  discount_factor: 0.99 # Discount factor for the rewards
+  gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, for action and value heads
+
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  parallel_envs: 8 # How many environments to run in parallel
+  number_of_steps: 128 # How many environment steps go into a single batch
+  batch_size: 256 # How many samples can go into the model once
+  experience_replay: 4 # How many times to replay the experience
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 2.5e-4
+  epsilon: 1.0e-5
+  max_grad_norm: 0.5 # Gradient clipping parameter
+
+
+scheduler:
+  name: vel.scheduler.linear_batch_scaler
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.1e7
+    batches_per_epoch: 10
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'atari_ppo_vid_{:04}.avi'
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    parallel_envs: 16 # How many environments to run in parallel
+
+    takes: 20
+
+  visdom:
+    name: vel.command.vis_store_command
diff --git a/examples-configs/rl/atari/atari_ppo_gru.yaml b/examples-configs/rl/atari/atari_ppo_gru.yaml
new file mode 100644
index 00000000..81d7af1e
--- /dev/null
+++ b/examples-configs/rl/atari/atari_ppo_gru.yaml
@@ -0,0 +1,79 @@
+name: 'atari_ppo_gru'
+
+
+env:
+  name: vel.rl.env.classic_atari
+  game: !param game = 'BreakoutNoFrameskip-v4'
+
+
+vec_env:
+  name: vel.rl.vecenv.shared_mem
+
+
+model:
+  name: vel.rl.policy.ppo_rnn
+
+  entropy_coefficient: 0.01
+  value_coefficient: 0.5
+
+  discount_factor: 0.99 # Discount factor for the rewards
+  gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
+
+  cliprange:
+    name: vel.function.linear
+    initial_value: 0.1
+    final_value: 0.0
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn
+      - name: vel.rl.layer.rnn_cell
+        hidden_size: 512
+        rnn_type: 'gru'
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, for action and value heads
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  parallel_envs: 8 # How many environments to run in parallel
+  number_of_steps: 128 # How many environment steps go into a single batch
+  batch_size: 256 # How many samples can go into the model once
+  experience_replay: 4 # How many times to replay the experience
+
+  shuffle_transitions: off  # Required for RNN policies
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 2.5e-4
+  epsilon: 1.0e-5
+  max_grad_norm: 0.5 # Gradient clipping parameter
+
+
+scheduler:
+  name: vel.scheduler.linear_batch_scaler
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.1e7
+    batches_per_epoch: 10
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'atari_ppo_gru_vid_{:04}.avi'
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    parallel_envs: 16 # How many environments to run in parallel
+
+    takes: 20
diff --git a/examples-configs/rl/atari/rainbow/breakout_rainbow.yaml b/examples-configs/rl/atari/atari_rainbow.yaml
similarity index 60%
rename from examples-configs/rl/atari/rainbow/breakout_rainbow.yaml
rename to examples-configs/rl/atari/atari_rainbow.yaml
index 1e0cbeec..78525aea 100644
--- a/examples-configs/rl/atari/rainbow/breakout_rainbow.yaml
+++ b/examples-configs/rl/atari/atari_rainbow.yaml
@@ -1,9 +1,9 @@
-name: 'breakout_rainbow'
+name: 'atari_rainbow'
 
 
 env:
   name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
+  game: !param game = 'BreakoutNoFrameskip-v4'
   settings:
     max_episode_frames: 108_000
 
@@ -14,7 +14,11 @@ vec_env:
 
 
 model:
-  name: vel.rl.models.q_rainbow_model
+  name: vel.rl.policy.rainbow
+
+  target_update_frequency: 32_000  # After how many batches to update the target network
+
+  discount_factor: 0.99
 
   atoms: 51  # 51 bins for Distributional DQN
   vmin: -10.0
@@ -23,31 +27,27 @@ model:
   initial_std_dev: 0.5
   factorized_noise: true
 
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.double_noisy_nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-    initial_std_dev: 0.5
-    factorized_noise: true
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.double_noisy_nature_cnn
+        # TODO(this should ideally be brought from level up)
+        initial_std_dev: 0.5
+        factorized_noise: true
 
 
 reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
+  name: vel.rl.reinforcer.buffered_off_policy_iteration_reinforcer
 
   env_roller:
     name: vel.rl.env_roller.transition_replay_env_roller
 
     # N-Step Q-Learning
     forward_steps: 3
-    discount_factor: 0.99
 
     replay_buffer:
-      name: vel.rl.buffers.prioritized_circular_replay_buffer
+      name: vel.rl.buffer.prioritized_circular_replay_buffer
 
       buffer_initial_size: 80_000 # How many samples we need in the buffer before we start using replay buffer
       buffer_capacity: 1_000_000
@@ -58,45 +58,37 @@ reinforcer:
 
       priority_exponent: 0.5
       priority_weight:
-        name: vel.schedules.linear
+        name: vel.function.linear
         initial_value: 0.4
         final_value: 1.0
 
       priority_epsilon: 1.0e-6
 
-  algo:
-    name: vel.rl.algo.distributional_dqn
-    double_dqn: true
-
-    target_update_frequency: 32_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
   rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
   training_steps: 32 # How many environment steps (per env) to perform per training round
   parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
 
 
 optimizer:
-  name: vel.optimizers.adam
+  name: vel.optimizer.adam
   lr: 6.25e-05
   epsilon: 1.5e-4
+  max_grad_norm: 0.5
 
 
 commands:
   train:
-    name: vel.rl.commands.rl_train_command
+    name: vel.rl.command.rl_train_command
     total_frames: 1.1e7  # 11M
     batches_per_epoch: 2500
 
   record:
-    name: vel.rl.commands.record_movie_command
+    name: vel.rl.command.record_movie_command
     takes: 10
-    videoname: 'breakout_rainbow_vid_{:04}.avi'
+    videoname: 'atari_rainbow_vid_{:04}.avi'
     fps: 15
 
   evaluate:
-    name: vel.rl.commands.evaluate_env_command
+    name: vel.rl.command.evaluate_env_command
     parallel_envs: 12
     takes: 20
diff --git a/examples-configs/rl/atari/atari_trpo.yaml b/examples-configs/rl/atari/atari_trpo.yaml
new file mode 100644
index 00000000..2fad825b
--- /dev/null
+++ b/examples-configs/rl/atari/atari_trpo.yaml
@@ -0,0 +1,81 @@
+name: 'atari_trpo'
+
+
+env:
+  name: vel.rl.env.classic_atari
+  game: !param game = 'BreakoutNoFrameskip-v4'
+
+
+vec_env:
+  name: vel.rl.vecenv.shared_mem
+  frame_history: 4  # How many stacked frames go into a single observation
+
+
+model:
+  name: vel.rl.policy.trpo
+
+  max_kl: 0.001
+  cg_iters: 10
+  line_search_iters: 10
+  improvement_acceptance_ratio: 0.1
+  cg_damping: 0.001
+  vf_iters: 3
+  entropy_coefficient: 0.01
+  discount_factor: 0.99
+
+  gae_lambda: 1.00 # Generalized Advantage Estimator Lambda parameter
+
+  policy_net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+      - name: vel.rl.layer.nature_cnn_small
+
+  value_net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.image_to_tensor
+        size: [84, 84, 4] # Number of channels is frame history
+      - name: vel.rl.layer.nature_cnn_small
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  # In openAI baselines they rollout single env for 512 steps.
+  # I roll out 16 envs for 32 steps in parallel
+  parallel_envs: 16 # How many environments to run in parallel
+  number_of_steps: 32 # How many environment steps go into a single batch
+  batch_size: 512
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 1.0e-4
+  epsilon: 1.0e-3
+#    max_grad_norm: 0.5
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.1e7
+    batches_per_epoch: 16
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'atari_trpo_vid_{:04}.avi'
+    frame_history: 4
+    sample_args:
+      argmax_sampling: true
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    takes: 100
+    frame_history: 4
+    sample_args:
+      argmax_sampling: true
diff --git a/examples-configs/rl/atari/dqn/breakout_dqn_distributional.yaml b/examples-configs/rl/atari/dqn/breakout_dqn_distributional.yaml
deleted file mode 100644
index a0c6e219..00000000
--- a/examples-configs/rl/atari/dqn/breakout_dqn_distributional.yaml
+++ /dev/null
@@ -1,90 +0,0 @@
-name: 'breakout_dqn_distributional'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.distributional_q_model
-
-  atoms: 51  # 51 bins for Distributional DQN
-  vmin: -10.0
-  vmax: 10.0
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 30_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 250_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.distributional_dqn
-
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 2.5e-4
-  alpha: 0.95
-  momentum: 0.95
-  epsilon: 1.0e-1
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7  # 11M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
diff --git a/examples-configs/rl/atari/dqn/breakout_dqn_raw.yaml b/examples-configs/rl/atari/dqn/breakout_dqn_raw.yaml
deleted file mode 100644
index 5422f454..00000000
--- a/examples-configs/rl/atari/dqn/breakout_dqn_raw.yaml
+++ /dev/null
@@ -1,86 +0,0 @@
-name: 'breakout_dqn_raw'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 30_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 250_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 2.5e-4
-  alpha: 0.95
-  momentum: 0.95
-  epsilon: 1.0e-1
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7  # 11M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
diff --git a/examples-configs/rl/atari/dqn/breakout_dueling_ddqn.yaml b/examples-configs/rl/atari/dqn/breakout_dueling_ddqn.yaml
deleted file mode 100644
index fb6088e0..00000000
--- a/examples-configs/rl/atari/dqn/breakout_dueling_ddqn.yaml
+++ /dev/null
@@ -1,79 +0,0 @@
-name: 'breakout_dueling_ddqn'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_dueling_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.double_nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 30_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 250_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    double_dqn: true
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 2.5e-4
-  alpha: 0.95
-  momentum: 0.95
-  epsilon: 1.0e-1
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7  # 11M
-    batches_per_epoch: 2500
diff --git a/examples-configs/rl/atari/dqn/seaquest_dqn_distributional.yaml b/examples-configs/rl/atari/dqn/seaquest_dqn_distributional.yaml
deleted file mode 100644
index 9d068b5c..00000000
--- a/examples-configs/rl/atari/dqn/seaquest_dqn_distributional.yaml
+++ /dev/null
@@ -1,90 +0,0 @@
-name: 'seaquest_dqn_distributional'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'SeaquestNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.distributional_q_model
-
-  atoms: 51  # 51 bins for Distributional DQN
-  vmin: -10.0
-  vmax: 10.0
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 30_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 250_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.distributional_dqn
-
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 2.5e-4
-  alpha: 0.95
-  momentum: 0.95
-  epsilon: 1.0e-1
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 5.0e7  # 11M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
diff --git a/examples-configs/rl/atari/dqn/seaquest_dqn_raw.yaml b/examples-configs/rl/atari/dqn/seaquest_dqn_raw.yaml
deleted file mode 100644
index a6e19031..00000000
--- a/examples-configs/rl/atari/dqn/seaquest_dqn_raw.yaml
+++ /dev/null
@@ -1,86 +0,0 @@
-name: 'seaquest_dqn_raw'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'SeaquestNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 30_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 250_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.rmsprop
-  lr: 2.5e-4
-  alpha: 0.95
-  momentum: 0.95
-  epsilon: 1.0e-1
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7  # 11M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'seaquest_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
diff --git a/examples-configs/rl/atari/dqn_rainbow_param/asterix_rp_dqn_distributional.yaml b/examples-configs/rl/atari/dqn_rainbow_param/asterix_rp_dqn_distributional.yaml
deleted file mode 100644
index 432cb64e..00000000
--- a/examples-configs/rl/atari/dqn_rainbow_param/asterix_rp_dqn_distributional.yaml
+++ /dev/null
@@ -1,93 +0,0 @@
-name: 'asterix_dqn_distributional'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'AsterixNoFrameskip-v4'
-  settings:
-    max_episode_frames: 108_000
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_distributional_model
-
-  atoms: 51  # 51 bins for Distributional DQN
-  vmin: -10.0
-  vmax: 10.0
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 80_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 1_000_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.distributional_dqn
-
-    target_update_frequency: 10_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 6.25e-05
-  epsilon: 1.5e-4
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 5.0e7  # 50M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    fps: 15
-    videoname: 'asterix_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 12
-    takes: 20
-
diff --git a/examples-configs/rl/atari/dqn_rainbow_param/asterix_rp_dqn_raw.yaml b/examples-configs/rl/atari/dqn_rainbow_param/asterix_rp_dqn_raw.yaml
deleted file mode 100644
index 4717e900..00000000
--- a/examples-configs/rl/atari/dqn_rainbow_param/asterix_rp_dqn_raw.yaml
+++ /dev/null
@@ -1,89 +0,0 @@
-name: 'asterix_rp_dqn_raw'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'AsterixNoFrameskip-v4'
-
-  settings:
-    max_episode_frames: 108_000
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 80_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 1_000_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    target_update_frequency: 32_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 6.25e-05
-  epsilon: 1.5e-4
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 5.0e7  # 50M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'asterix_vid_{:04}.avi'
-    fps: 15
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 12
-    takes: 20
diff --git a/examples-configs/rl/atari/dqn_rainbow_param/asteroids_rp_dqn_noisynet.yaml b/examples-configs/rl/atari/dqn_rainbow_param/asteroids_rp_dqn_noisynet.yaml
deleted file mode 100644
index b71afb46..00000000
--- a/examples-configs/rl/atari/dqn_rainbow_param/asteroids_rp_dqn_noisynet.yaml
+++ /dev/null
@@ -1,87 +0,0 @@
-name: 'asteroids_rp_dqn_noisynet'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'AsteroidsNoFrameskip-v4'
-
-  settings:
-    max_episode_frames: 108_000
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.noisy_q_model
-
-  initial_std_dev: 0.5
-  factorized_noise: true
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.noisy_nature_cnn
-
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-    initial_std_dev: 0.5
-    factorized_noise: true
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 80_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 1_000_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    target_update_frequency: 32_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 6.25e-05
-  epsilon: 1.5e-4
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 5.0e7  # 50M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'asterix_vid_{:04}.avi'
-    fps: 15
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 12
-    takes: 20
diff --git a/examples-configs/rl/atari/dqn_rainbow_param/asteroids_rp_dqn_raw.yaml b/examples-configs/rl/atari/dqn_rainbow_param/asteroids_rp_dqn_raw.yaml
deleted file mode 100644
index 9622d200..00000000
--- a/examples-configs/rl/atari/dqn_rainbow_param/asteroids_rp_dqn_raw.yaml
+++ /dev/null
@@ -1,89 +0,0 @@
-name: 'asteroids_rp_dqn_raw'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'AsteroidsNoFrameskip-v4'
-
-  settings:
-    max_episode_frames: 108_000
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 80_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 1_000_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    target_update_frequency: 32_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 6.25e-05
-  epsilon: 1.5e-4
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 5.0e7  # 50M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'asterix_vid_{:04}.avi'
-    fps: 15
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 12
-    takes: 20
diff --git a/examples-configs/rl/atari/dqn_rainbow_param/atlantis_rp_dqn_nstep.yaml b/examples-configs/rl/atari/dqn_rainbow_param/atlantis_rp_dqn_nstep.yaml
deleted file mode 100644
index d6558654..00000000
--- a/examples-configs/rl/atari/dqn_rainbow_param/atlantis_rp_dqn_nstep.yaml
+++ /dev/null
@@ -1,92 +0,0 @@
-name: 'atlantis_rp_dqn_nstep'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'AtlantisNoFrameskip-v4'
-
-  settings:
-    max_episode_frames: 108_000
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    # N-Step Q-Learning
-    forward_steps: 3
-    discount_factor: 0.99
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 80_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 1_000_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    target_update_frequency: 32_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 6.25e-05
-  epsilon: 1.5e-4
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 5.0e7  # 50M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'atlantis_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 12
-    takes: 100
diff --git a/examples-configs/rl/atari/dqn_rainbow_param/atlantis_rp_dqn_raw.yaml b/examples-configs/rl/atari/dqn_rainbow_param/atlantis_rp_dqn_raw.yaml
deleted file mode 100644
index d30461e2..00000000
--- a/examples-configs/rl/atari/dqn_rainbow_param/atlantis_rp_dqn_raw.yaml
+++ /dev/null
@@ -1,88 +0,0 @@
-name: 'atlantis_rp_dqn_raw'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'AtlantisNoFrameskip-v4'
-
-  settings:
-    max_episode_frames: 108_000
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.q_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_initial_size: 80_000 # How many samples we need in the buffer before we start using replay buffer
-      buffer_capacity: 1_000_000
-
-      # Because env has a framestack already built-in, save memory by encoding only last frames in the replay buffer
-      frame_stack_compensation: true
-      frame_history: 4  # How many stacked frames go into a single observation
-
-    action_noise:
-      name: vel.rl.modules.noise.eps_greedy
-
-      epsilon:
-        name: vel.schedules.linear_and_constant
-        end_of_interpolation: 0.1
-        initial_value: 1.0
-        final_value: 0.1
-
-  algo:
-    name: vel.rl.algo.dqn
-
-    target_update_frequency: 32_000  # After how many batches to update the target network
-    max_grad_norm: 0.5
-
-    discount_factor: 0.99
-
-  rollout_steps: 4 # How many environment steps (per env) to perform per batch of training
-  training_steps: 32 # How many environment steps (per env) to perform per training round
-  parallel_envs: 1  # Roll out only one env in parallel, just like in DeepMind paper
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 6.25e-05
-  epsilon: 1.5e-4
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 5.0e7  # 50M
-    batches_per_epoch: 2500
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'atlantis_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 12
-    takes: 100
diff --git a/examples-configs/rl/atari/ppo/breakout_ppo.yaml b/examples-configs/rl/atari/ppo/breakout_ppo.yaml
deleted file mode 100644
index 1a1c4e50..00000000
--- a/examples-configs/rl/atari/ppo/breakout_ppo.yaml
+++ /dev/null
@@ -1,80 +0,0 @@
-name: 'breakout_ppo'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-
-    cliprange:
-      name: vel.schedules.linear
-      initial_value: 0.1
-      final_value: 0.0
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 8 # How many environments to run in parallel
-  number_of_steps: 128 # How many environment steps go into a single batch
-  batch_size: 256 # How many samples can go into the model once
-  experience_replay: 4 # How many times to replay the experience
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 2.5e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 10
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_ppo_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
diff --git a/examples-configs/rl/atari/ppo/breakout_ppo_gru.yaml b/examples-configs/rl/atari/ppo/breakout_ppo_gru.yaml
deleted file mode 100644
index 1a7aa669..00000000
--- a/examples-configs/rl/atari/ppo/breakout_ppo_gru.yaml
+++ /dev/null
@@ -1,84 +0,0 @@
-name: 'breakout_ppo_gru'
-
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-
-
-model:
-  name: vel.rl.models.stochastic_policy_rnn_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn_rnn
-    rnn_type: 'gru'
-    hidden_units: 512
-
-    input_width: 84
-    input_height: 84
-    input_channels: 1  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-
-    cliprange:
-      name: vel.schedules.linear
-      initial_value: 0.1
-      final_value: 0.0
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 8 # How many environments to run in parallel
-  number_of_steps: 128 # How many environment steps go into a single batch
-  batch_size: 256 # How many samples can go into the model once
-  experience_replay: 4 # How many times to replay the experience
-
-  shuffle_transitions: off  # Required for RNN policies
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 2.5e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 10
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_ppo_gru_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
diff --git a/examples-configs/rl/atari/ppo/enduro_ppo.yaml b/examples-configs/rl/atari/ppo/enduro_ppo.yaml
deleted file mode 100644
index afc6d1cc..00000000
--- a/examples-configs/rl/atari/ppo/enduro_ppo.yaml
+++ /dev/null
@@ -1,85 +0,0 @@
-name: 'enduro_ppo'
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'EnduroNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-
-    cliprange:
-      name: vel.schedules.linear
-      initial_value: 0.1
-      final_value: 0.0
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 8 # How many environments to run in parallel
-  number_of_steps: 128 # How many environment steps go into a single batch
-  batch_size: 256 # How many samples can go into the model once
-  experience_replay: 4 # How many times to replay the experience
-
-  discount_factor: 0.99 # Discount factor for the rewards
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 2.5e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 10
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'enduro_ppo_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
-
-  enjoy:
-    name: vel.rl.commands.enjoy
-    fps: 15
diff --git a/examples-configs/rl/atari/ppo/qbert_ppo.yaml b/examples-configs/rl/atari/ppo/qbert_ppo.yaml
deleted file mode 100644
index b4e012b0..00000000
--- a/examples-configs/rl/atari/ppo/qbert_ppo.yaml
+++ /dev/null
@@ -1,81 +0,0 @@
-name: 'qbert_ppo_simple'
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'QbertNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  backbone:
-    name: vel.rl.models.backbone.nature_cnn
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.01
-    value_coefficient: 0.5
-
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-
-    cliprange:
-      name: vel.schedules.linear
-      initial_value: 0.1
-      final_value: 0.0
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 8 # How many environments to run in parallel
-  number_of_steps: 128 # How many environment steps go into a single batch
-  batch_size: 256 # How many samples can go into the model once
-  experience_replay: 4 # How many times to replay the experience
-
-  discount_factor: 0.99 # Discount factor for the rewards
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 2.5e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 10
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'qbert_ppo_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    parallel_envs: 16 # How many environments to run in parallel
-
-    takes: 20
diff --git a/examples-configs/rl/atari/trpo/breakout_trpo.yaml b/examples-configs/rl/atari/trpo/breakout_trpo.yaml
deleted file mode 100644
index 9c29af38..00000000
--- a/examples-configs/rl/atari/trpo/breakout_trpo.yaml
+++ /dev/null
@@ -1,90 +0,0 @@
-name: 'breakout_trpo'
-
-env:
-  name: vel.rl.env.classic_atari
-  game: 'BreakoutNoFrameskip-v4'
-
-
-vec_env:
-  name: vel.rl.vecenv.shared_mem
-  frame_history: 4  # How many stacked frames go into a single observation
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model_separate
-
-  input_block:
-    name: vel.modules.input.image_to_tensor
-
-  policy_backbone:
-    name: vel.rl.models.backbone.nature_cnn_small
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-  value_backbone:
-    name: vel.rl.models.backbone.nature_cnn_small
-    input_width: 84
-    input_height: 84
-    input_channels: 4  # The same as frame_history
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.trpo
-    max_kl: 0.001
-    cg_iters: 10
-    line_search_iters: 10
-    improvement_acceptance_ratio: 0.1
-    cg_damping: 0.001
-    vf_iters: 3
-    entropy_coef: 0.1
-    discount_factor: 0.99
-
-#    max_grad_norm: 0.5
-    gae_lambda: 1.00 # Generalized Advantage Estimator Lambda parameter
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  # In openAI baselines they rollout single env for 512 steps.
-  # I roll out 16 envs for 32 steps in parallel
-  parallel_envs: 16 # How many environments to run in parallel
-  number_of_steps: 32 # How many environment steps go into a single batch
-  batch_size: 512
-
-
-optimizer:
-#  name: vel.optimizers.rmsprop
-#  lr: 7.0e-4
-#  alpha: 0.99
-##  epsilon: 1.0e-5
-#  epsilon: 1.0e-3
-
-  name: vel.optimizers.adam
-  lr: 1.0e-4
-  epsilon: 1.0e-3
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.1e7
-    batches_per_epoch: 16
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'breakout_trpo_vid_{:04}.avi'
-    frame_history: 4
-    sample_args:
-      argmax_sampling: true
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    frame_history: 4
-    sample_args:
-      argmax_sampling: true
diff --git a/examples-configs/rl/mujoco/a2c/reacher_a2c.yaml b/examples-configs/rl/mujoco/a2c/reacher_a2c.yaml
deleted file mode 100644
index f3a313ef..00000000
--- a/examples-configs/rl/mujoco/a2c/reacher_a2c.yaml
+++ /dev/null
@@ -1,67 +0,0 @@
-name: 'reacher_a2c'
-
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'Reacher-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 11
-
-  backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.a2c
-
-    entropy_coefficient: 0.0
-    value_coefficient: 0.5
-    max_grad_norm: 0.5
-
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-    discount_factor: 0.99 # Discount factor for the rewards
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 1 # How many environments to run in parallel
-  number_of_steps: 2048 # How many environment steps go into a single batch
-  batch_size: 2048 # How many samples can go into the model once
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 3.0e-4
-  epsilon: 1.0e-5
-
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 1
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'reacher_vid_{:04}.avi'
-    sample_args:
-      argmax_sampling: true
diff --git a/examples-configs/rl/mujoco/ddpg/half_cheetah_ddpg.yaml b/examples-configs/rl/mujoco/ddpg/half_cheetah_ddpg.yaml
deleted file mode 100644
index 978227b5..00000000
--- a/examples-configs/rl/mujoco/ddpg/half_cheetah_ddpg.yaml
+++ /dev/null
@@ -1,93 +0,0 @@
-name: 'half_cheetah_ddpg'
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'HalfCheetah-v2'
-#  normalize_returns: true
-#  normalize_observations: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.deterministic_policy_model
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 17
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 17
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-#    normalization: 'layer'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 23  # Has to be observation size(17) + action size(6)
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-#    normalization: 'layer'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer
-
-  env_roller:
-    name: vel.rl.env_roller.transition_replay_env_roller
-
-    replay_buffer:
-      name: vel.rl.buffers.circular_replay_buffer
-
-      buffer_capacity: 1_000_000
-      buffer_initial_size: 2_000
-
-    normalize_returns: true
-    discount_factor: 0.99
-
-    action_noise:
-      name: vel.rl.modules.noise.ou_noise
-      std_dev: 0.2
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ddpg
-
-    tau: 0.01
-    discount_factor: 0.99
-
-  rollout_steps: 2
-  training_steps: 64
-
-  parallel_envs: 1
-
-
-optimizer:
-  name: vel.optimizers.adam
-  # OpenAI has two different optimizers optimizing each network separately.
-  # As far as I know it should be equivalent to optimizing two separate networks together with a sum of loss functions
-  lr: [1.0e-4, 1.0e-3, 1.0e-3]
-  weight_decay: [0.0, 0.0, 0.01]
-  epsilon: 1.0e-4
-  layer_groups: on
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 1000
-
-#    openai_logging: true
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'half_cheetah_vid_{:04}.avi'
-
-  evaluate:
-    name: vel.rl.commands.evaluate_env_command
-    takes: 100
-    frame_history: 4
diff --git a/examples-configs/rl/mujoco/mujoco_a2c.yaml b/examples-configs/rl/mujoco/mujoco_a2c.yaml
new file mode 100644
index 00000000..f85a277a
--- /dev/null
+++ b/examples-configs/rl/mujoco/mujoco_a2c.yaml
@@ -0,0 +1,60 @@
+name: 'mujoco_a2c'
+
+
+env:
+  name: vel.rl.env.mujoco
+  game: !param game = 'Reacher-v2'
+
+
+vec_env:
+  name: vel.rl.vecenv.dummy
+  normalize_returns: true
+
+
+model:
+  name: vel.rl.policy.a2c
+
+  entropy_coefficient: 0.0
+  value_coefficient: 0.5
+  gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
+  discount_factor: 0.99 # Discount factor for the rewards
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.normalize_ewma
+      - name: vel.net.layer.mlp
+        hidden_layers: [64, 64]
+        activation: 'tanh'
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, for action and value heads
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  parallel_envs: 1 # How many environments to run in parallel
+  number_of_steps: 2048 # How many environment steps go into a single batch
+  batch_size: 2048 # How many samples can go into the model once
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 3.0e-4
+  epsilon: 1.0e-5
+  max_grad_norm: 0.5
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.0e6
+    batches_per_epoch: 1
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'reacher_vid_{:04}.avi'
diff --git a/examples-configs/rl/mujoco/mujoco_ddpg.yaml b/examples-configs/rl/mujoco/mujoco_ddpg.yaml
new file mode 100644
index 00000000..a5c837e0
--- /dev/null
+++ b/examples-configs/rl/mujoco/mujoco_ddpg.yaml
@@ -0,0 +1,93 @@
+name: 'mujoco_ddpg'
+
+
+env:
+  name: vel.rl.env.mujoco
+  game: !param game = 'Reacher-v2'
+
+
+vec_env:
+  name: vel.rl.vecenv.dummy
+  normalize_returns: true
+
+
+model:
+  name: vel.rl.policy.ddpg
+
+  tau: 0.01
+  discount_factor: 0.99
+  noise_std_dev: 0.2
+
+  input_net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.normalize_ewma
+
+  actor_net:
+    name: vel.net.modular
+    group: 'actor'
+    layers:
+      - name: vel.net.layer.mlp
+        hidden_layers: [64, 64]
+        activation: 'tanh'
+
+  critic_net:
+    name: vel.net.modular
+    group: 'critic'
+    layers:
+      - name: vel.net.layer.util.concat # Concatenate observation and action
+      - name: vel.net.layer.mlp
+        hidden_layers: [64, 64]
+        activation: 'tanh'
+
+
+reinforcer:
+  name: vel.rl.reinforcer.buffered_off_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.transition_replay_env_roller
+
+    replay_buffer:
+      name: vel.rl.buffer.circular_replay_buffer
+
+      buffer_capacity: 1_000_000
+      buffer_initial_size: 2_000
+
+    normalize_returns: true
+    discount_factor: 0.99
+
+  rollout_steps: 2
+  training_steps: 64
+
+  parallel_envs: 1
+
+
+optimizer:
+  name: vel.optimizer.adam
+  # OpenAI has two different optimizers optimizing each network separately.
+  # As far as I know it should be equivalent to optimizing two separate networks together with a sum of loss functions
+  lr: 1.0e-3
+  weight_decay: 0.0
+  epsilon: 1.0e-4
+  parameter_groups:
+    actor:
+      lr: 1.0e-4
+    critic:
+      lr: 1.0e-3
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.0e6
+    batches_per_epoch: 1000
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'half_cheetah_vid_{:04}.avi'
+
+  evaluate:
+    name: vel.rl.command.evaluate_env_command
+    takes: 100
+    frame_history: 4
diff --git a/examples-configs/rl/mujoco/mujoco_ppo.yaml b/examples-configs/rl/mujoco/mujoco_ppo.yaml
new file mode 100644
index 00000000..5fdbd7bd
--- /dev/null
+++ b/examples-configs/rl/mujoco/mujoco_ppo.yaml
@@ -0,0 +1,73 @@
+name: 'mujoco_ppo'
+
+
+env:
+  name: vel.rl.env.mujoco
+  game: !param game = 'Reacher-v2'
+
+
+vec_env:
+  name: vel.rl.vecenv.dummy
+  normalize_returns: true
+
+
+model:
+  name: vel.rl.policy.ppo
+
+  entropy_coefficient: 0.0
+  value_coefficient: 0.5
+
+  cliprange: 0.2
+
+  discount_factor: 0.99 # Discount factor for the rewards
+  gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.normalize_ewma
+      - name: vel.net.layer.util.repeat
+        times: 2 # Need to repeat output twice, to consume by the 'parallel' layers
+      - name: vel.net.layer.arch.parallel
+        layers:
+          - name: vel.net.layer.mlp
+            hidden_layers: [64, 64]
+            activation: 'tanh'
+          - name: vel.net.layer.mlp
+            hidden_layers: [64, 64]
+            activation: 'tanh'
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  parallel_envs: 1 # How many environments to run in parallel
+  batch_size: 64 # How many samples can go into the model in one batch
+  number_of_steps: 2048 # How many environment steps go into a single batch
+  experience_replay: 10 # How many times to replay the experience
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 3.0e-4
+  epsilon: 1.0e-5
+  max_grad_norm: 0.5 # Gradient clipping parameter
+
+
+scheduler:
+  name: vel.scheduler.linear_batch_scaler
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.0e6
+    batches_per_epoch: 1
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'half_cheetah_vid_{:04}.avi'
diff --git a/examples-configs/rl/mujoco/mujoco_trpo.yaml b/examples-configs/rl/mujoco/mujoco_trpo.yaml
new file mode 100644
index 00000000..5c2a83ee
--- /dev/null
+++ b/examples-configs/rl/mujoco/mujoco_trpo.yaml
@@ -0,0 +1,78 @@
+name: 'mujoco_trpo'
+
+
+env:
+  name: vel.rl.env.mujoco
+  game: !param game = 'Reacher-v2'
+
+
+vec_env:
+  name: vel.rl.vecenv.dummy
+  normalize_returns: true
+
+
+model:
+  name: vel.rl.policy.trpo
+
+  discount_factor: 0.99 # Discount factor for the rewards
+  gae_lambda: 0.98 # Generalized Advantage Estimator Lambda parameter
+
+  max_kl: 0.01
+  cg_iters: 10
+  line_search_iters: 10
+  improvement_acceptance_ratio: 0.1
+  cg_damping: 0.1
+  vf_iters: 5
+  entropy_coefficient: 0.0
+
+  input_net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.input.normalize_ewma
+
+  policy_net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.mlp
+        hidden_layers: [32, 32]
+        activation: 'tanh'
+
+  value_net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.mlp
+        hidden_layers: [32, 32]
+        activation: 'tanh'
+
+
+reinforcer:
+  name: vel.rl.reinforcer.on_policy_iteration_reinforcer
+
+  env_roller:
+    name: vel.rl.env_roller.step_env_roller
+
+  parallel_envs: 1 # How many environments to run in parallel
+  number_of_steps: 1024 # How many environment steps go into a single batch
+  batch_size: 1024 # How many samples can go into the model once
+
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 1.0e-3
+  epsilon: 1.0e-8
+  #    max_grad_norm: 0.5
+
+
+commands:
+  train:
+    name: vel.rl.command.rl_train_command
+    total_frames: 1.0e6
+    batches_per_epoch: 4
+
+  record:
+    name: vel.rl.command.record_movie_command
+    takes: 10
+    videoname: 'reacher_vid_{:04}.avi'
+    sample_args:
+      argmax_sampling: true
diff --git a/examples-configs/rl/mujoco/ppo/half_cheetah_ppo.yaml b/examples-configs/rl/mujoco/ppo/half_cheetah_ppo.yaml
deleted file mode 100644
index 202e0d4f..00000000
--- a/examples-configs/rl/mujoco/ppo/half_cheetah_ppo.yaml
+++ /dev/null
@@ -1,77 +0,0 @@
-name: 'half_cheetah_ppo'
-
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'HalfCheetah-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model_separate
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 17
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 17
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 17
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.0
-    value_coefficient: 0.5
-
-    cliprange: 0.2
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 1 # How many environments to run in parallel
-  batch_size: 64 # How many samples can go into the model in one batch
-  number_of_steps: 2048 # How many environment steps go into a single batch
-  experience_replay: 10 # How many times to replay the experience
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 3.0e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 1
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'half_cheetah_vid_{:04}.avi'
diff --git a/examples-configs/rl/mujoco/ppo/hopper_ppo.yaml b/examples-configs/rl/mujoco/ppo/hopper_ppo.yaml
deleted file mode 100644
index 2001d0c9..00000000
--- a/examples-configs/rl/mujoco/ppo/hopper_ppo.yaml
+++ /dev/null
@@ -1,77 +0,0 @@
-name: 'hopper_ppo'
-
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'Hopper-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model_separate
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 11
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.0
-    value_coefficient: 0.5
-
-    cliprange: 0.2
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 1 # How many environments to run in parallel
-  batch_size: 64 # How many samples can go into the model once
-  number_of_steps: 2048 # How many environment steps go into a single batch
-  experience_replay: 10 # How many times to replay the experience
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 3.0e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 1
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'hopper_vid_{:04}.avi'
diff --git a/examples-configs/rl/mujoco/ppo/reacher_ppo.yaml b/examples-configs/rl/mujoco/ppo/reacher_ppo.yaml
deleted file mode 100644
index f0da8742..00000000
--- a/examples-configs/rl/mujoco/ppo/reacher_ppo.yaml
+++ /dev/null
@@ -1,77 +0,0 @@
-name: 'reacher_ppo'
-
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'Reacher-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model_separate
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 11
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.0
-    value_coefficient: 0.5
-
-    cliprange: 0.2
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 1 # How many environments to run in parallel
-  batch_size: 64 # How many samples can go into the model once
-  number_of_steps: 2048 # How many environment steps go into a single batch
-  experience_replay: 10 # How many times to replay the experience
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 3.0e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 1
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'reacher_vid_{:04}.avi'
diff --git a/examples-configs/rl/mujoco/ppo/walker_ppo.yaml b/examples-configs/rl/mujoco/ppo/walker_ppo.yaml
deleted file mode 100644
index a07777f0..00000000
--- a/examples-configs/rl/mujoco/ppo/walker_ppo.yaml
+++ /dev/null
@@ -1,77 +0,0 @@
-name: 'walker_ppo'
-
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'Walker2d-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model_separate
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 17
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 17
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 17
-    hidden_layers: [64, 64]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.ppo
-
-    entropy_coefficient: 0.0
-    value_coefficient: 0.5
-
-    cliprange: 0.2
-
-    max_grad_norm: 0.5 # Gradient clipping parameter
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.95 # Generalized Advantage Estimator Lambda parameter
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 1 # How many environments to run in parallel
-  batch_size: 64 # How many samples can go into the model once
-  number_of_steps: 2048 # How many environment steps go into a single batch
-  experience_replay: 10 # How many times to replay the experience
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 3.0e-4
-  epsilon: 1.0e-5
-
-
-scheduler:
-  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 1
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'walker_vid_{:04}.avi'
diff --git a/examples-configs/rl/mujoco/trpo/half_cheetah_trpo.yaml b/examples-configs/rl/mujoco/trpo/half_cheetah_trpo.yaml
deleted file mode 100644
index 131bb6e3..00000000
--- a/examples-configs/rl/mujoco/trpo/half_cheetah_trpo.yaml
+++ /dev/null
@@ -1,81 +0,0 @@
-name: 'half_cheetah_trpo'
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'HalfCheetah-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.stochastic_policy_model_separate
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 17
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 17
-    hidden_layers: [32, 32]
-    activation: 'tanh'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 17
-    hidden_layers: [32, 32]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.trpo
-    discount_factor: 0.99 # Discount factor for the rewards
-    gae_lambda: 0.98 # Generalized Advantage Estimator Lambda parameter
-
-    max_kl: 0.01
-    cg_iters: 10
-    line_search_iters: 10
-    improvement_acceptance_ratio: 0.1
-    cg_damping: 0.1
-    vf_iters: 5
-    entropy_coef: 0.0
-#    max_grad_norm: 0.5
-
-  env_roller:
-    name: vel.rl.env_roller.step_env_roller
-
-  parallel_envs: 1 # How many environments to run in parallel
-  number_of_steps: 1024 # How many environment steps go into a single batch
-  batch_size: 1024 # How many samples can go into the model once
-#  experience_replay: 10 # How many times to replay the experience
-
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 1.0e-3
-  epsilon: 1.0e-8
-
-
-#scheduler:
-#  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 4
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'reacher_vid_{:04}.avi'
-    sample_args:
-      argmax_sampling: true
diff --git a/examples-configs/rl/mujoco/trpo/hopper_trpo.yaml b/examples-configs/rl/mujoco/trpo/hopper_trpo.yaml
deleted file mode 100644
index 41444d79..00000000
--- a/examples-configs/rl/mujoco/trpo/hopper_trpo.yaml
+++ /dev/null
@@ -1,82 +0,0 @@
-name: 'hopper_trpo'
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'Hopper-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.policy_gradient_model_separate
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 17
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [32, 32]
-    activation: 'tanh'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [32, 32]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.trpo
-    max_kl: 0.01
-    cg_iters: 10
-    line_search_iters: 10
-    improvement_acceptance_ratio: 0.1
-    cg_damping: 0.1
-    vf_iters: 5
-    entropy_coef: 0.0
-#    max_grad_norm: 0.5
-
-  env_roller:
-    name: vel.rl.env_roller.vec.step_env_roller
-    gae_lambda: 0.98 # Generalized Advantage Estimator Lambda parameter
-
-
-  parallel_envs: 1 # How many environments to run in parallel
-  batch_size: 1024 # How many samples can go into the model once
-  number_of_steps: 1024 # How many environment steps go into a single batch
-#  experience_replay: 10 # How many times to replay the experience
-
-  discount_factor: 0.99 # Discount factor for the rewards
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 0.001
-  epsilon: 1.0e-8
-
-
-#scheduler:
-#  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 2
-    openai_logging: true
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'reacher_vid_{:04}.avi'
-    sample_args:
-      argmax_sampling: true
diff --git a/examples-configs/rl/mujoco/trpo/reacher_trpo.yaml b/examples-configs/rl/mujoco/trpo/reacher_trpo.yaml
deleted file mode 100644
index 65aa7bc0..00000000
--- a/examples-configs/rl/mujoco/trpo/reacher_trpo.yaml
+++ /dev/null
@@ -1,81 +0,0 @@
-name: 'reacher_trpo'
-
-env:
-  name: vel.rl.env.mujoco
-  game: 'Reacher-v2'
-  normalize_returns: true
-
-
-vec_env:
-  name: vel.rl.vecenv.dummy
-
-
-model:
-  name: vel.rl.models.policy_gradient_model_separate
-
-  input_block:
-    name: vel.modules.input.normalize_observations
-    input_shape: 17
-
-  policy_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [32, 32]
-    activation: 'tanh'
-
-  value_backbone:
-    name: vel.rl.models.backbone.mlp
-    input_length: 11
-    hidden_layers: [32, 32]
-    activation: 'tanh'
-
-
-reinforcer:
-  name: vel.rl.reinforcers.on_policy_iteration_reinforcer
-
-  algo:
-    name: vel.rl.algo.policy_gradient.trpo
-    max_kl: 0.01
-    cg_iters: 10
-    line_search_iters: 10
-    improvement_acceptance_ratio: 0.1
-    cg_damping: 0.1
-    vf_iters: 5
-    entropy_coef: 0.0
-#    max_grad_norm: 0.5
-
-  env_roller:
-    name: vel.rl.env_roller.vec.step_env_roller
-    gae_lambda: 0.98 # Generalized Advantage Estimator Lambda parameter
-
-  parallel_envs: 1 # How many environments to run in parallel
-  batch_size: 1024 # How many samples can go into the model once
-  number_of_steps: 1024 # How many environment steps go into a single batch
-#  experience_replay: 10 # How many times to replay the experience
-
-  discount_factor: 0.99 # Discount factor for the rewards
-
-
-optimizer:
-  name: vel.optimizers.adam
-  lr: 0.001
-  epsilon: 1.0e-8
-
-
-#scheduler:
-#  name: vel.scheduler.linear_batch_scaler
-
-
-commands:
-  train:
-    name: vel.rl.commands.rl_train_command
-    total_frames: 1.0e6
-    batches_per_epoch: 2
-    openai_logging: true
-
-  record:
-    name: vel.rl.commands.record_movie_command
-    takes: 10
-    videoname: 'reacher_vid_{:04}.avi'
-    sample_args:
-      argmax_sampling: true
diff --git a/examples-configs/text-classification/imdb_sentiment_gru.yaml b/examples-configs/text-classification/imdb_sentiment_gru.yaml
new file mode 100644
index 00000000..8fa86a95
--- /dev/null
+++ b/examples-configs/text-classification/imdb_sentiment_gru.yaml
@@ -0,0 +1,71 @@
+name: 'imdb_sentiment_gru'
+
+
+source:
+  name: vel.data.source.nlp.imdb
+  vocab_size: 25_000
+
+
+loader:
+  name: vel.data.loader.bucket_loader
+  batch_size: 32
+
+
+model:
+  name: vel.model.nlp.sequence_classification
+  output_dim: 2 # Positive or negative sentiment
+
+  net:
+    name: vel.net.modular
+    layers:
+      - name: vel.net.layer.nlp.pretrained_embedding
+        group: "embedding"
+        vectors: "glove.6B.100d"  # precomputed 100-dimensional embeddings
+
+      - name: vel.net.layer.rnn
+        group: "rnn"
+        hidden_size: 256
+        rnn_type: 'gru'
+        bidirectional: true
+
+      - name: vel.net.layer.dropout
+        p: 0.3
+
+      - name: vel.net.layer.rnn
+        group: "rnn"
+        hidden_size: 128
+        rnn_type: 'gru'
+        bidirectional: true
+
+      # For sequence classification we only want outputs for the last hidden state
+      - name: vel.net.layer.nlp.select_final_features
+        bidirectional: true
+
+      - name: vel.net.layer.dropout
+        p: 0.3
+
+      - name: vel.net.layer.mlp
+        hidden_layers: [64]
+        activation: 'relu'
+
+      - name: vel.net.layer.dropout
+        p: 0.2
+
+
+optimizer:
+  name: vel.optimizer.adam
+  lr: 1.0e-2
+  weight_decay: 0.001
+  epsilon: 1.0e-5
+  betas: [0.7, 0.99]
+
+  parameter_groups:
+    embedding: {"lr": 1.0e-4, "weight_decay": 0.0}
+    rnn: {"lr": 1.0e-3, "weight_decay": 0.0001}
+
+
+commands:
+  train:
+    name: vel.command.train_command
+    max_grad_norm: 5.0
+    epochs: 10
diff --git a/examples-notebooks/autoencoders/mnist/mnist-autoencoder.ipynb b/examples-notebooks/autoencoders/mnist/mnist-autoencoder.ipynb
new file mode 100644
index 00000000..ea642bf8
--- /dev/null
+++ b/examples-notebooks/autoencoders/mnist/mnist-autoencoder.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import vel\n",
+    "import vel.notebook as nb\n",
+    "nb.reasonable_notbook_defaults()\n",
+    "torch.set_grad_enabled(False)  # We don't need autograd here\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = nb.load_config('examples-configs/autoencoder/mnist/mnist_cnn_ae.yaml', run_number=1, device='cpu')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = config.load_trained_model()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MnistCnnAutoencoder(\n",
+      "  (encoder): Sequential(\n",
+      "    (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (1): ReLU(inplace=True)\n",
+      "    (2): Conv2d(8, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+      "    (3): ReLU(inplace=True)\n",
+      "    (4): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+      "    (5): Flatten()\n",
+      "    (6): Linear(in_features=784, out_features=16, bias=True)\n",
+      "  )\n",
+      "  (decoder): Sequential(\n",
+      "    (0): Linear(in_features=16, out_features=784, bias=True)\n",
+      "    (1): ReLU(inplace=True)\n",
+      "    (2): Reshape(sizes=(16, 7, 7), batch_dims=1)\n",
+      "    (3): ConvTranspose2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
+      "    (4): ReLU(inplace=True)\n",
+      "    (5): ConvTranspose2d(16, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
+      "    (6): ReLU(inplace=True)\n",
+      "    (7): ConvTranspose2d(8, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (8): Sigmoid()\n",
+      "  )\n",
+      ")\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Number of model parameters: 33,009\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_loader = config.provide('loader')\n",
+    "data_source = data_loader.transformed_source\n",
+    "train_dataset = data_source.train\n",
+    "validation_dataset = data_source.validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_sample(idx):\n",
+    "    return train_dataset[idx]['x'].to(config.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_image(axis, sample):\n",
+    "    axis.imshow(train_dataset.denormalize_item(sample, 'x'), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Browse examples\n",
+    "fig, axes = plt.subplots(1, 5)\n",
+    "\n",
+    "for index in range(5):\n",
+    "    show_image(axes[index], get_sample(index))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Browse examples\n",
+    "fig, axes = plt.subplots(2, 5)\n",
+    "\n",
+    "for index in range(5):\n",
+    "    sample = get_sample(index)\n",
+    "    decoded = model(sample[None])[0]\n",
+    "    show_image(axes[0, index], sample)\n",
+    "    show_image(axes[1, index], decoded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "samples = torch.randn(5, model.representation_length)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 5)\n",
+    "\n",
+    "for index in range(5):\n",
+    "    decoded = model.decoder(samples[index][None])[0].detach()\n",
+    "    show_image(axes[index], decoded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-7.0867e-03,  9.9738e+00, -6.5140e+00,  4.5889e+00, -4.8899e+00,\n",
+       "          3.6933e-01, -2.5673e+00,  1.4296e+01,  8.0464e-01,  1.1216e+01,\n",
+       "          6.2219e+00,  3.5970e+00, -1.4565e+00, -1.0004e+01, -4.6819e+00,\n",
+       "          1.0152e-01]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " model.encode(get_sample(0)[None])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idx1 = 1\n",
+    "idx2 = 5\n",
+    "\n",
+    "N = 10\n",
+    "\n",
+    "encoding1 = model.encode(get_sample(idx1)[None])\n",
+    "encoding2 = model.encoder(get_sample(idx2)[None])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, N)\n",
+    "\n",
+    "for i in range(10):\n",
+    "    beta = float(i) / float(N - 1)\n",
+    "    alpha = 1.0 - beta\n",
+    "    \n",
+    "    combined =  model.decoder(encoding1 * alpha + encoding2 * beta)[0]\n",
+    "    \n",
+    "    show_image(axes[i], combined)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples-notebooks/autoencoders/mnist/mnist-vae.ipynb b/examples-notebooks/autoencoders/mnist/mnist-vae.ipynb
new file mode 100644
index 00000000..652fd859
--- /dev/null
+++ b/examples-notebooks/autoencoders/mnist/mnist-vae.ipynb
@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import tqdm\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import vel\n",
+    "import vel.notebook as nb\n",
+    "nb.reasonable_notbook_defaults()\n",
+    "torch.set_grad_enabled(False)  # We don't need autograd here\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = nb.load_config('examples-configs/latent/mnist/mnist_fc_vae.yaml', run_number=1, device='cuda:0')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = config.load_trained_model().to(config.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FcVae(\n",
+      "  (encoder): Sequential(\n",
+      "    (0): Flatten()\n",
+      "    (1): Linear(in_features=784, out_features=200, bias=True)\n",
+      "    (2): Tanh()\n",
+      "    (3): Linear(in_features=200, out_features=200, bias=True)\n",
+      "    (4): Tanh()\n",
+      "    (5): Linear(in_features=200, out_features=100, bias=True)\n",
+      "  )\n",
+      "  (decoder): Sequential(\n",
+      "    (0): Linear(in_features=50, out_features=200, bias=True)\n",
+      "    (1): Tanh()\n",
+      "    (2): Linear(in_features=200, out_features=200, bias=True)\n",
+      "    (3): Tanh()\n",
+      "    (4): Linear(in_features=200, out_features=784, bias=True)\n",
+      "    (5): Reshape(sizes=(1, 28, 28), batch_dims=1)\n",
+      "    (6): Sigmoid()\n",
+      "  )\n",
+      ")\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Number of model parameters: 425,284\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_loader = config.provide('loader')\n",
+    "data_source = data_loader.transformed_source\n",
+    "train_dataset = data_source.train\n",
+    "validation_dataset = data_source.validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_sample(idx):\n",
+    "    return train_dataset[idx]['x'].to(config.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_image(axis, sample):\n",
+    "    axis.imshow(train_dataset.denormalize_item(sample, 'x'), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Browse examples\n",
+    "fig, axes = plt.subplots(1, 5)\n",
+    "\n",
+    "for index in range(5):\n",
+    "    show_image(axes[index], get_sample(index).cpu())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 0.5881, -1.6104,  0.4107,  1.0962,  1.7997,  1.0594,  0.0516,  1.5038,\n",
+      "          2.1698,  0.0210, -0.3525,  0.4182,  1.5991,  0.0826,  1.5369,  0.0597,\n",
+      "          2.2284,  0.1273, -1.6312, -0.1385,  1.0510,  0.0295, -0.2376, -0.0673,\n",
+      "          0.5040,  0.7474, -0.1831,  0.7763,  0.1418,  1.6395, -2.4175,  0.7683,\n",
+      "          0.1352, -2.3757, -0.4010, -2.0714,  0.0345, -0.5530,  1.6734, -1.6953,\n",
+      "          2.1401,  1.4186,  0.7702,  0.0874, -0.8720, -1.3320, -1.0852, -0.1516,\n",
+      "         -1.0706,  1.0755]], device='cuda:0')\n",
+      "torch.Size([1, 50])\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = model.encode(get_sample(0)[None])\n",
+    "print(x)\n",
+    "print(x.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Browse examples\n",
+    "fig, axes = plt.subplots(2, 5)\n",
+    "\n",
+    "for index in range(5):\n",
+    "    sample = get_sample(index)\n",
+    "    decoded = model(sample[None])[0].detach()\n",
+    "    show_image(axes[0, index], sample.cpu())\n",
+    "    show_image(axes[1, index], decoded.cpu())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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U7NCVrARkh6jsSharVq0K78Aor9g6e/jhhzUePHgwAKBjx46a69Chg8Zjx44FAJx99tmas/Upq67YFYWeffZZjR955BGNZRi/U6dOmrNtYNJyMHfuXM2VtioXka1p25oitbht2zbNvfTSSxrLii67du3SXKoVgyy78oq0WAHJWh8zZozmhgwZUuK1uN9NfpNzV5MmTTT3gx/8QGPZu2bTpk2a69Kli8bSAmNX1LIrC0o7Ye3atTUn+yYAwT1vpIXFnmfttYO8lj2P2xZZ+fuxx2prnvKPnRZg261kTyV7/rLteOVdCct+f9tSeOmll2p89913AwheE9jvf9999wEA3n//fc1lq82VIyFERERERBQq3oQQEREREVGoqlQ7liXDpHZVDDvcZDeEIUqXbV2R4dbHH39cc7Y1S1ZzsStmLFq0SGNZ/cquCCSbZQHBleCkrtu0aaM5u1mhtLzYlbjKapMhAoKtK3LOnDlzpuY++OADjWXjtXRry36dbJho22hk01kg2fLCdqz8Ju0mrVq10lxhYaHGcp5r0aKF5i666KISH7erY9makfd+ew1gW1xsG5itRWFXh5N2G/u97Aax0u5i/2ZYv/nNXmfadit5T7ZtUXbjS2mtSrWiIJBcqe2oo47S3FlnnaXxbbfdprG0YdnvNWHCBI0ffPDBg75WJnEkhIiIiIiIQlVlR0LsmsgijLs+qlrsU4+ioiIAwKBBgzRnn+Y1b94cQPBJiOyzACQn99qnw6VNFpMnKHYSpd1/QRZmWL16dTl/EqrK7Iix3bujWbNmAICtW7dqzj7JrexkRvskcMCAAQCAhg0bam7dunUa8/xdtaxcuVJjuwDItddeCyA4GdySUQ07irxixQqNN27cCCC5VwIQfLo8dOhQjWU05eWXX9ac7PcEAGvWrAEQ/DuwC4DI3wprt2qS+gCSI2S2Y+GCCy7QWEbTbN3az5Ua7dq1q+bOOOMMjeX6AkjW4+uvv665W2+9VWPbVZFtHAkhIiIiIqJQ8SaEiIiIiIhCVWXbsWQSsJ0wZttgpF2FKNNsndmWAhmate1Wdpi+Iq0t0orQr18/zdnJlbI+/ebNm8v9PanqsrVnF05o0KABgOAEXVtnFalZ+To7YVj21gGAG2+8EUBwYq9tgwmzhYByR2pK2qYA4C9/+YvG//znPwOfBwTf56UdqrTalNZD284le48AwcU+CgoKAADjx4/XnF04pLx7O1DVYevuo48+0lhqpXHjxprr3bu3xkceeSQA4N1339Wcff+WxRn69u2rObtwh22plff/6667TnO5WoypzJEQ51wL59w059xi59wi59zoRL6Bc26Kc2554r/1y/peRGFgzVLcsGYpblizFEes22gpTzvWfgC3eu87A+gN4AbnXGcAYwBM9d53ADA18W+iKGDNUtywZiluWLMUR6zbCCmzHct7vwHAhkS82zm3BEAhgMEAzkx82lMA3gJwe1aOMgu2bNkCILiqi20D4NB+fMWpZm3rVSbXh+/QoQMA4Nhjj9WcXWlo6tSpAJJrk1NuRb1mbQuBXTVIWlNuueUWzdkWAWlNsW2Fsp49ALRu3VrjCy+8EEByPx0AOOaYYzSWv5X7779fc3PmzEl5jJR9uapZ+T3b37fsR3NgnA6pVVuzdhXB+fPnayyrG8rKhQd+HUVPlM61dqUrqRt7HWpbqGR1K1vfdp8Rad2yqwfalkJbo7/4xS8AJK+Dc6lCc0Kcc60BdAcwG0BB4pcJABsBFJTyNSMBjEz/EInSx5qluGHNUtywZimOWLe5V+6bEOdcHQDPAbjZe7/rgMmH3jmX8jGU934cgHGJ7xGZR1Uy0mEnoNetW1dj2Z0SCE6EpPjIt5oti32CcvXVVwMIThi2ox5vv/02AE6cjJo41Oy8efM0lvqyoxd2nfpVq1YBCJ5D7SRfu3a91K99urdw4UKN77rrLgDA9OnTNWf3XKDciEPNVpYsZAMkd0EHkudU+5SZ4iEKdWvPi2+88QYAYMSIEQf9XLswiN0R/dRTTwUA1K5dW3P2/PjMM89oLAt6RGHUrlxL9DrnqqP4lzXRe/98Ir3JOdc08fGmALjMDkUGa5bihjVLccOapThi3UZHeVbHcgCeBLDEe/+A+dBkAHLLNgLAS5k/PKKKY81S3LBmKW5YsxRHrNtocWVN5nPO9QHwNoAFAGQW7U9R3EM3CUBLAEUAhnjvt5fxvSIz5CpDVjNmzNCcXVO5Z8+eGq9fvz68A0ua673vkYsXjrt8rVmZWG4nmNuJaz16JMvl6aefBgC0bNlSc7I2OACcdtppAIBNmzZl8hBZs2mKU83a+rv88ssBJCc6AkD9+smVLWVipK1TG9tzq7QLTJw4UXN2H51MLtxgsGbTFKearSzbqiOT0QGgUaNGAIDFixdrzra9ZmmxBNZsJUS1bmUfkJtvvllzp5xyisbSjmUX9ujUqZPGLVq0ABA8P7/zzjsay8IfQHBxkbB4712qfHlWx5oJIOUXA+hXSp4oZ1izFDesWYob1izFEes2Wso1J4SIiIiIiChTKrREbz6RVYFk9RYguGeDHfIiigJZycKu/W1XF5IVsYBkS8z27cnR5BdeeEHjvXv3Zu04Kb/ZFVWk7c+2UNWsWVNjaTGwLQK29uzqhFFYqYUoFdtWtWHDBo2lnTBLrYJUhUiL1N133625Zs2aaSytWdJKDQRXdJWVsOzeSbfddluJ7x81HAkhIiIiIqJQVfmRkOHDh2uuQYMGGttdf4mixI7Y2R3Ru3TporE8ubPrkNs9F/jUmTLJ1qSdmGtjonxgRz2yNPGcqjC7t8fGjRs1/vjjjwEA9erV09y6des0lt3PJ0+erLmtW7dm7TgzhSMhREREREQUKt6EEBERERFRqKpsO5b48ssvNbYTzoiiyrZS2RarV155ReOTTjoJAPDuu+9qbvr06RpzIiURUcWxBYvCYluzZBGE559/XnN2z6XPPvsMQPCaNg61ypEQIiIiIiIKFW9CiIiIiIgoVC7M4ZpMbnFfBcz13vfI9UFUdazZCmHNRgBrtkJYsxHAmq0Q1mxEsG7Lz3ufcpd6joQQEREREVGoeBNCRERERESh4k0IERERERGFijchREREREQUqrD3CdkKYG/iv/mmETL7c7XK4Pei9LFmy481Gw1bARQh87/fKGDN5ifWbPmxZqMjX68PQqvZUFfHAgDn3Jx8XNkhX38uyt/fbb7+XFQsH3+/+fgzUVI+/n7z8WeipHz8/Yb5M7Edi4iIiIiIQsWbECIiIiIiClUubkLG5eA1w5CvPxfl7+82X38uKpaPv998/JkoKR9/v/n4M1FSPv5+Q/uZQp8TQkREREREVRvbsYiIiIiIKFS8CSEiIiIiolCFehPinOvvnFvqnFvhnBsT5mtninOuhXNumnNusXNukXNudCLfwDk3xTm3PPHf+rk+Vqo81izFTT7ULMC6rUpYsxQ3rNkMvX5Yc0Kcc9UALANwDoB1AN4HMMx7vziUA8gQ51xTAE299x845+oCmAvgIgBXAdjuvb8vUZD1vfe35/BQqZJYsxQ3+VKzAOu2qmDNUtywZjMnzJGQkwCs8N6v9N5/BeAfAAaH+PoZ4b3f4L3/IBHvBrAEQCGKf5anEp/2FIp/iRRvrFmKm7yoWYB1W4WwZiluWLMZEuZNSCGAtebf6xK52HLOtQbQHcBsAAXe+w2JD20EUJCjw6LMYc1S3ORdzQKs2zzHmqW4Yc1mCCemp8k5VwfAcwBu9t7vsh/zxT1uXPuYIoU1S3HEuqW4Yc1S3OSqZit1E1LBiTmfAmhh/t08kYsd51x1FP+yJnrvn0+kNyV666THbnOujo9Kx5plzcZRBeo2b2oWYN3GGWuWNRs3rNnwazbtm5DExJxHAQwA0BnAMOdc54N8yfsAOjjn2jjnagAYCmByuq+fK845B+BJAEu89w+YD00GMCIRjwDwUtjHRgfHmmXNxlEF6zYvahZg3cYZa5Y1Gzes2dzUbNqrYznnTgbwC+/9eYl/3wEA3vt7D/I1HIIsv63e+8a5Poh8wprNOtZsFlS0blmzFcKazQLWbFaxZrMgjZo9H8Cr4R1hvHnvXap8ZdqxyjUxxzk30jk3xzk3pxKvVRUV5foA8hBrNrtYs9lRZt2yZtPGms0O1mz2sGazo0LXBwB+FdaB5bNDs/0C3vtxAMYBfNpB8cCapbhhzVLcsGYpjli3mVWZkZC8mphDVQJrluKIdUtxw5qluGHN5kBlbkLyZmIOVRmsWYoj1i3FDWuW4oY1mwNpt2N57/c7524E8AaAagDGe+8XZezIiDKMNUtxxLqluGHNUtywZnMj7dWx0nox9s9VxFzvfY9cH0RVx5qtENZsBLBmK4Q1GwGs2QphzUYE67b8srE6FhERERERUYVlfXWsfFCrVi0AQIsWyTlLe/fu1Xj9+vUAgDBHlSg/HHJI8jlAQUEBAKBPnz6aa9asmcarVq0CALz99tua++yzzzRm/REREVFccCSEiIiIiIhCxZsQIiIiIiIKFduxStGgQQONX375ZQBA69atNffvf/9b41GjRgEAvv7663AOjmLNtmB17NhR4wceeAAAcNppp2nusMMO01jarWwL1uTJyRUEx44dqzFbBImIiOLFueT87Tp16mh88cUXAwC6du2qOduaPXXqVADA7t27NVfW+799rVSx/frS4sriSAgREREREYWKIyHGoYcm/3fceuutGnfv3h1A8E6xRo0aGn/77bchHB3li+rVq2s8aNAgjU855RQAQO3atTVna06ePthRuuHDh2t8/PHHa3zppZcCAIqKijJ12EREZMi5/IgjjtCcvR7Ys2cPAHZJUPnVrVtX47///e8an3zyyQCC9XXkkUdq/M477wAILppU2rWpdGPUrFlTc02aNNG4Xbt2AIANGzZozl5L2NGWyuJICBERERERhYo3IUREREREFKoq345l21169EhuQnrVVVdpLEOuX3zxheZee+01jdmORRVhh0A7d+6ssdTi/v37NWdjqTPbCmhbu0444QSNX3jhBQBAv379NLdjx45KHzsRUVVTrVo1jZs2baqxTBY+/fTTNffVV19p/OKLLwIAXnnlFc19/vnnWTtOij/bbm33ppM2bWnxA4CZM2dqLO/vpV2Pppp4bq8fevXqpbHUtW27koVzAGDRokUAMjNBnSMhREREREQUKt6EEBERERFRqKp8O5ZdXeDOO+/U2K52IStbLF26VHPTpk3TmHsxUEXYlVKmT5+usaxIYfcGmTt3rsbffPMNAKBv376as3vX2K+TtcTvv/9+zV1zzTWVPXSiwLC+tKnYFkFbh9K60qdPH81dfvnlGh977LEaS2vAxo0bNXfPPfdoPHHiRADBFkWibLGrZdqVB3/xi19o3Lt3bwBArVq1NCfnaQAoLCwEAMyZM0dzq1at0pjXDnQge+3ZsGHDEh+3bdV2nxC5riitpsra5+PEE0/U+OyzzwYQbC386KOPNJZ2rEzgSAgREREREYWqyo6EyDrJAwcO1Jw81QCCT/ZkreQxY8ZobsuWLdk+RMpTpS1wIDuh24lny5cv11hq0n7NTTfdpPFZZ52lsTxVlglmAHDjjTdqvG/fvvR/AKoyZKTDjhgPGDBA4wsuuAAA0KVLF83ZibvyVM9OgCyLfRI4btw4jYcMGQIA+N73vqe5bdu2lfv7EpWHjPQdd9xxmvvnP/+psZ0sLNcRdvRj165dGsvEdLvfAkc/6GDsSK8djZO6lOsEIDgqUpEFkqQGbS1269ZN48MPPxxAcIQv1f5lnJhORERERESxw5sQIiIiIiIKVZVtx5Ih/9tuu01z9evX19gOM02ZMgUAMGPGDM1xbxBKl60tO5wqa37bdeTtJHZhJ+7u3LlT4+7du2t81FFHAUgOqwLBiWd2fXEiy048l8US/vGPf2jOTiaXdi1b0/brpV2lPFK1CNh2BFmQwR6LbTFct24dgGC7oyXHaidbEh1I9nF6+umnNde2bVuNbX1L68yKFSs0N2rUKI1nzZoFgNcLVH516tRJGYslS5ZobN//K0Jq2O5JYvcsS3Vety2H9m+gssp8h3DOjXfObXbOLTS5Bs65Kc655Yn/1j/Y9ypI4qAAACAASURBVCAKE2uW4oh1S3HDmqW4Yc1GS3keU00A0P+A3BgAU733HQBMTfybKComgDVL8TMBrFuKlwlgzVK8TABrNjLKbMfy3s9wzrU+ID0YwJmJ+CkAbwG4PYPHlRUyxAQA9957LwDg6KOPTvlxuwLBgw8+CIDD+HERp5q1Q5xSc3boPtXa3nb1jIUL9WEONm3apLG0Y9maPu200zRmO1b0RKVubQtVz549AQBt2rTRnK0pqV/bQvjll1+WiG3b4YIFCzS2ey8VFBQAAC666CLNSR3b4+rUqZPmzjjjDI3/+9//lvhZGjVqpPGaNWsAAOvXry/xeZSeqNRsZdn2kkcffRRAsD3F/k3Y87PU74UXXqi51atXZ+swKQOiWrNSg0OHDtWcXVVQzrV///vfS+QqSurZ1q1tzZJjsed1u1JnJtsL050TUuC9lzXnNgIoKO0TnXMjAYxM83WIMoU1S3FUrrplzVKEsGYpbnh9kCOVnpjuvffOuVIXC/bejwMwDgAO9nlhsOsgy669qe40AeCRRx7R2E4EoviLUs2WNYnXjnqUtSb33r17S3yu/ZpsrfNN4ThY3WayZu15cNq0aQCCe9O0b99e49mzZwMAJk+erDm7I/TmzZsBBCeL25qz+zGdd955AIBLLrkk5efKqMqHH36oublz52ose4bYEWu7n9Pu3btB4QqrZiurefPmGl9xxRUAgudjW4e2vvv16wcgWecUf7m6PqhXrx6A4EiwrUGZhP7ee+/ZY0nrtWTxBXuutSPcMtIho8dAcAQ7k9JdoneTc64pACT+y79AijrWLMUR65bihjVLccOazZF0b0ImAxiRiEcAeCkzh0OUNaxZiiPWLcUNa5bihjWbI2W2Yznn/o7iCTuNnHPrAIwFcB+ASc65awAUARiSzYOsDNvucvfdd2ss6y/bCTZ2H5B77rlH43Qn/1BuRLVmpRZtC6DsVwMAtWrVAhAcYt2+fbvG0o5iP273AbG1LjVra3fx4sWV+wEoq6JYtxs2FLdJ270PbFufTFy0+9nYdig5v9qalToHgMsuu0zjX/3qVwCCEyTtxEjZH+SXv/yl5rZu3VritbgnQ3iiWLPp+MlPfqKxnJ9L289p+PDhGrMNK36iVLP2PVvasOxiGrYGZTGZXbt2Vfp1ZcGPVq1apfy4nM+lHffA17XXMAd+DVCxNrHyrI41rJQP9Sv3qxCFiDVLccS6pbhhzVLcsGajJd12LCIiIiIiorRUenWsqLPtLnafBFl1YM+ePZobPXq0xvv27Qvh6Cjf2dUt6tcv3oR1yJDkSO/AgQM1lmFY285i9z6YNGkSgOAeNscdd1zK15LViOyKWcuWLdOYq2JReUid2KF4e86UmrNtBdahhxa/xRQWFmru6aef1vjEE0/UWIb47bC+XaXwrrvuAhDch4QoE15//XWNU62cOWXKFI2ztUoQVT22nVra/OyKgfb9+8knnwSQfrupvT6Qa2F7fWyvCaQN1q4oaD9XzsGpVuSs8HGl9VVERERERERpyvuRkDvuuENjO6FS2HWQi4qKQjkmqjpkF2gAuP/++wEAgwYN0txhhx120K+3T4plBMXuk2DX9m7cuLHG8tSjtJ2suU8IpSvVkzj79K5JkyYaDxgwAEBwMnmqnXmB5GjL448/rjkZ/QA4AkLZ884772gs59eOHTtqLtUCIUTpsOe8wYMHa9y5c2cAwffkFStWaDx//vwSHy+LHf045ZRTNJbzcd26dVMel5zj7XldRrWB5Gh1JhYB4UgIERERERGFijchREREREQUqrxtx5JhpOuuuy7lx2X/BNvaQpQJtkXqpptu0ljasFK1BQKp96OxQ6Bt2rQBADRt2lRzdjjUTnKTr6tXr57mOnTooLFMUreTgFNhuxYdjAzhy7rzAPCzn/1M42HDilfDtG2Hts5Xr16tsewT8tJLyX3C2PpCYbDn7BYtWgAITsS1bbVElWHPhTfccIPGsnedXZhm3rx5Jb7OtrPa9+eaNWsCAHr16qU5u9iSzcvnWvv379d448aNAIJ75+3cubPE52bi+oAjIUREREREFCrehBARERERUajyth2rb9++AIJDX3a46eOPPwYAPPXUU5qz64LblQLYkkIVYevo1FNPLZG3LVBbtmzRePHixQCCe9S0bdtWY1l1qFatWpqzLVi2pUDYYdcrrriixNfZdsTNmzdrLOt/23YY+/dDVZc9N0pr4WWXXaY5G8v5155D7UpD9957r8YvvvgigOCKbkTZYlcO6tGjh8ZynrbnO7uKkG2R5TmRKsq+pzdv3lxjmUJg27H69OmjcadOnQAA7dq105xtzZK6tHVtpdrLybbGzp49W2Np41q+fLnm7HGxHYuIiIiIiGKLNyFERERERBSqvGrHssNNsiqLHS5du3atxqNGjQIQHG6qX7++xnaYyq4KQFQWu2LVZ599prG0YX3xxReae/bZZzV+4oknAAA7duzQnN2A8Hvf+x6A4AZHhYWFKY9BhmRti5bdrEhWM7IrEf3f//2fxrJqkf1Z7N8EWxSrLtuOcswxxwAALrzwQs3ZdgCpeduCKCuzAcDMmTM1lr8L1haFwa5S2LBhQ43nzJkDILhRbCY2ZaOqTc6L55xzjuZsm5+8V9spBLb16uijjwaQuq3KKu38afNyXfzGG29obsSIERrLdUt5vldlcSSEiIiIiIhClVcjIXZd77POOgtA8KnzY489pvHChQsBBCf5nn/++RrbUZPXXnsNQHBiDlFp7OjbW2+9pbFMMrNPEexTjT179gAAdu3apTlbc7Jmt90nxE5ca9SokcYyyS3VJGIg+TTbjm7YmpcnIXZiOp9QV122juxT45NPPhlAcMTNLqwgCyDY0ZFt27ZpbEf9+LSZwiATz2WhDyC4QMiqVasAAL1799acPbfaJ9X2+oLoYFJ1J9hzpbw/23Ntqknm9n3Yvj9v2rQJALB7927NyZ43QPKaAABWrFgBALjxxhs1Z8/FYb7XcySEiIiIiIhCxZsQIiIiIiIKVV61Y11yySUay4Qfu/fB+++/r/GRRx4JIDih8tJLL9V4+vTpGr/++uuZP1jKW7atZPLkyRpfffXVAIJDpBdccIHG0hIwbdo0zdmhWfk6u7a43Qck1d42tt3KDrfK34KtbduSwNZDsmzbaseOHTWWWrQTLO3nSguA3fvjP//5j8Z20Q+2+1G22PNkmzZtAAAtW7bUnN3bSRZbsHswSKsLwHMjVY6dDN66dWuNU7VrS4s2kKw72+JtF7aRNvAf/OAHmrv22ms1tq1bcg62dZ2r82+ZIyHOuRbOuWnOucXOuUXOudGJfAPn3BTn3PLEf+uX9b2IwsCapbhhzVLcsGYpjli30VKedqz9AG713ncG0BvADc65zgDGAJjqve8AYGri30RRwJqluGHNUtywZimOWLcRUmY7lvd+A4ANiXi3c24JgEIAgwGcmfi0pwC8BeD2rBzlQdhh1uuvv15jWZVF2q6A4NCUrKQlQ69AcJjVtmPZYSyKvijVrOy3AQDjx48HAPz4xz/WXLNmzTSW/PDhwzVnV7qQmrWrY9kVL+xKGlLLdp+SDz74QOMXX3wRALBmzZoSX0Phi1LNWlJTnTt31pxdT15WELI1aVcPkiH+9evXa27KlCka25XkKF6iWrNC9kICkq2wAFBQUAAg2Opiz8Oy4ps9n9pzp13RiOInV3Ur57rFixdrbuzYsRpLDUp9AsBHH32ksayaafdcsmQFt/bt22vOtsbauv34448Dx5RLFZoT4pxrDaA7gNkAChK/TADYCKCglK8ZCWBk+odIlD7WLMUNa5bihjVLccS6zb1y34Q45+oAeA7Azd77XQdMgvXOuZSzWrz34wCMS3yPjM98qVOnjsatWrXSWPZBsGvaDxo0SGO5AywqKtLcz3/+c43t5GBOmIynKNSsnRgu+9Rs3bpVc3feeafGMlGybdu29lg0lidz9gmd/f72yZ6McNgdqd98802NFyxYAIDr3EdNFGrWkgm7Q4cO1dy5556rsTx9sxN7U+1988c//lFzK1euzNThUQRErWZldPiRRx7RXL9+/ezrAgguimD3bpDuCjt6d9ddd5X4eoq3XNWtXbhm+/btGsvCMXakpKy9k+wxN27cGABw3HHHpfxcu/+YdGjIdTKQu1GRci3R65yrjuJf1kTv/fOJ9CbnXNPEx5sC2Fza1xOFjTVLccOapbhhzVIcsW6jozyrYzkATwJY4r1/wHxoMgBpDh4B4KXMHx5RxbFmKW5YsxQ3rFmKI9ZttJSnHetUAFcCWOCc+zCR+ymA+wBMcs5dA6AIwJDsHOLB2aFRO7QlE9Ltx+2EnldeeQVAsAXLDr9yyDXWIlmz0vr0t7/9TXN2AYQ//OEPAIDTTz9dc7Vr19ZYhl7tBHK7t8d///tfjWX98KVLl6b8XJmkxjqPjEjWrExs7Nu3r+bsxEk7nC/27t2r8e9//3sAwLhx4zRXVosBxUYka1bOk506ddKc3cdGPi6L1wDBc+qGDcXTAq677jrNrV27NjsHS7kQybqV9+KKvCfbdiyZkG6nKNjvZVu35VrYLiJir4/lHB3G9UF5VseaCcCV8uF+peSJcoY1S3HDmqW4Yc1SHLFuo6Vcc0KIiIiIiIgypUJL9EaRrCgAAAMHDtT4yiuvBBBs0Zo0aZLGMuTK1gAKmx3itPt0XHLJJQCC69sfffTRGsvqF9u2bdPc8uXLNbarvUi7la1v+7p2GJeoNLLSkB22t6Sm7CprDz30kMa/+c1vAHCvJQqPtFbZ9/tbbrlFY9uaJdatW6fxNddcAwB47733NMe2VYoi+z4u0wnsNa/dR8zuOSbXB7ZFy666mU5rWLo4EkJERERERKFyYd7hZ2OfkDw213vfI9cHUdWxZiuENRsBmaxZmbx7++3JjYNllBlIPlH79a9/rbnnnntOYzvhN6JYsxGQjfOsnXj+u9/9TmNZ+GPWrFmak0VBgOQ+DRHukmDNRkSUrg9kr5sWLVporrCwUGPbNbRs2TIA4e4N4r1P2X7BkRAiIiIiIgoVb0KIiIiIiChUbMeKLg65RgBrtkJYsxGQyZqtXr06AKBXr16a69atm8bvvvsuAGDRokWakxatmGDNRkC2z7N2Aq/EEW63KgtrNiJ4fVB+bMciIiIiIqJI4E0IERERERGFKvb7hBARUXZI64qsGAQE97bZunUrgOA+IURRY9vOuecHUXRwJISIiIiIiELFkRAiIkpJ9vmwu/DamIiIKF0cCSEiIiIiolDxJoSIiIiIiEIVdjvWVgB7E//NN42Q2Z+rVQa/F6WPNVt+rNlo2AqgCJn//UYBazY/sWbLjzUbHfl6fRBazYa6WSEAOOfm5ONGO/n6c1H+/m7z9eeiYvn4+83Hn4mS8vH3m48/EyXl4+83zJ+J7VhERERERBQq3oQQEREREVGocnETMi4HrxmGfP25KH9/t/n6c1GxfPz95uPPREn5+PvNx5+JkvLx9xvazxT6nBAiIiIiIqra2I5FRERERESh4k0IERERERGFKtSbEOdcf+fcUufcCufcmDBfO1Occy2cc9Occ4udc4ucc6MT+QbOuSnOueWJ/9bP9bFS5bFmKW7yoWYB1m1VwpqluGHNZuj1w5oT4pyrBmAZgHMArAPwPoBh3vvFoRxAhjjnmgJo6r3/wDlXF8BcABcBuArAdu/9fYmCrO+9vz2Hh0qVxJqluMmXmgVYt1UFa5bihjWbOWGOhJwEYIX3fqX3/isA/wAwOMTXzwjv/Qbv/QeJeDeAJQAKUfyzPJX4tKdQ/EukeGPNUtzkRc0CrNsqhDVLccOazZAwb0IKAaw1/16XyMWWc641gO4AZgMo8N5vSHxoI4CCHB0WZQ5rluIm72oWYN3mOdYsxQ1rNkM4MT1Nzrk6AJ4DcLP3fpf9mC/ucePaxxQprFmKI9YtxQ1rluImVzVbqZuQCk7M+RRAC/Pv5olc7DjnqqP4lzXRe/98Ir0p0VsnPXabc3V8VDrWLGs2jipQt3lTswDrNs5Ys6zZuGHNhl+zad+EJCbmPApgAIDOAIY55zof5EveB9DBOdfGOVcDwFAAk9N9/VxxzjkATwJY4r1/wHxoMoARiXgEgJfCPjY6ONYsazaOKli3eVGzAOs2zlizrNm4Yc3mpmbTXh3LOXcygF94789L/PsOAPDe33uQr+EQZPlt9d43zvVB5BPWbNaxZrOgonXLmq0Q1mwWsGazijWbBWnU7PkAXg3vCOPNe+9S5SvTjlWuiTnOuZHOuTnOuTmVeK2qqCjXB5CHWLPZxZrNjjLrljWbNtZsdrBms4c1mx0Vuj4A8KuwDiyfHZrtF/DejwMwDuDTDooH1izFDWuW4oY1S3HEus2syoyE5NXEHKoSWLMUR6xbihvWLMUNazYHKnMTkjcTc6jKYM1SHLFuKW5YsxQ3rNkcSLsdy3u/3zl3I4A3AFQDMN57vyhjR0aUYaxZiiPWLcUNa5bihjWbG2mvjpXWi7F/riLmeu975PogqjrWbIWwZiOANVshrNkIYM1WCGs2Ili35ZeN1bGIiIiIiIgqLOurYxERERGF6ZBDDkkZf/PNNwCAMLtAiDKleG/BYkceeaTGtWvXBgA0aNBAc9WqVdN42bJlGn/xxRcAovE3wJEQIiIiIiIKFW9CiIiIiIgoVFW2HUuGqexwlR2yPfTQ5P8aGb7dv3+/5uwwlsSpcgfGRERElDn2vfuwww4DAHTp0kVzjRs31viDDz4AAOzYsUNz8h5v4/K8h/O9nTLN1nLdunU1Hj16NADg+9//vuaOOuoojeX69PPPP9fc4YcfrvGePXs0vuGGGwAAL7zwQqYOO20cCSEiIiIiolBVqZGQgoICjX/yk58AAM4991zNycQeANi5c6fGn332GYDgxB77BETuQFeuXKm5l19+WePVq1cHPo+ovOwktOrVqwMA6tWrp7kWLZIbvH799dcar127FkCydgE+tSOi/GG7GNq1a6fxkCFDAABnnXWW5pYuXarxvn37ACRHRIDkRF0AqFGjRonvb68NLLlOkO8J8DxLlWPr58svv9RY3tPfffddzdn39xkzZgAITkwfOXKkxs2bN9e4V69eAIDJk5N7MdrRwDBxJISIiIiIiELFmxAiIiIiIgpV3rdjSQsLANx2220a//CHPwQA1KpVS3O2ncVOCBInnXSSxnaim3zu3r17Nde6dWuN77rrLgDAli1bKnz8VHVIG0DHjh01d/XVV2vct29fAEBhYaHm7MSzb7/9VuN169YBAP7f//t/mnv++edTfi5RJtkWQrvAhyhtgQ+iimjUqJHGjz/+uMY9ehRvJm5bVebMmaNxUVERgOD7tT0fSn3a2rXXEe3bt9dY9mn48MMPNbdp06aK/ihEyp4TbZvgX//6VwDA3/72N82lOpfa8+/06dM1vvnmmzWeNWsWgNT754SNIyFERERERBQq3oQQEREREVGo8r4dS9YMB4Bu3bppLK0vdhjWDt++/vrrGq9YsQJAsE3mggsu0LhOnToAgqtpSA7I3TAXRZMdAm3ZsqXGf/7znwEAffr00ZxtAyiLHcaVlgHbpmA/Lq1ZbIehTGvVqpXGf/jDHwAE17Nfs2aNxra14O233wYA7Nq1S3OsTzqQtJucc845muvZs6fG0mL9ySefaO6ZZ57RuLyrVdr2bNsWY/MXX3wxgGQLGAA8+uijGttrCqoa5NoSAL766quMfV+5jizretKeMzds2KDxe++9p7GcY6NwbcqRECIiIiIiClXej4TYO9F58+Zp3LVrVwDA7t27NTdu3DiN//3vf2ssT6PtSIgdYZEnM/ZpybPPPquxfbJHVZfUid3b47XXXtO4U6dOgc8DUq8ZbuvJPmk74ogjNJa9RGTiJAD85je/0VjWFOdiCZQJdhLvE088obHs1WBr1u7XZM+psm/DhAkTNGfXxOc+SwQkz4/2Ka49D0p3w3PPPae5jz/+WON06sieh20tv/POOwCAH//4x5o7+eSTNX7jjTcCx0T5L5OjH+mwe9rInjkAsHnzZo3nz58PIBp1yZEQIiIiIiIKFW9CiIiIiIgoVHnfjmW3vX/44Yc13rdvHwBg5cqVmnvrrbc0tpPM+/XrBwA499xzNZdqfxE7mV3aXYBoTP6h3JOaGj16tObatWunsbQZ2ImPdn17WSd88eLFmrPDqWeeeabGI0eOBAA0bdpUc3Zy8IknngggWLNE6bJ71xx//PEay/n1vvvu05ydMHzKKado3L9/fwDJBRqA4N/Km2++mcEjpriSc57dA+GVV17RWFqsx48frzl7HVBZtjVLWlzs9+/Vq5fGch1g9yQhygZpbR07dqzm7J41EydO1DhKUwTKHAlxzo13zm12zi00uQbOuSnOueWJ/9bP7mESlR9rluKIdUtxw5qluGHNRkt52rEmAOh/QG4MgKne+w4Apib+TRQVE8CapfiZANYtxcsEsGYpXiaANRsZZbZjee9nOOdaH5AeDODMRPwUgLcA3J7B48qK9evXa/z73/8eQLBVyrZY2TaBO++8E0CwncWSlgO7QgaHX3MnqjUr7Vi2NuzqbDKk/8gjj2jOrhS0Y8cOAMF2ALsqkV0h5tRTTwUANGzYUHN2f5IzzjgDADBlyhTNsW0wt6Jat+Vx2mmnafzpp59qfO+99wIAXn31Vc3Z+rWrs8lKWq1bt9bc0KFDNZ46dSoA1mmU5LJmN27cqPFdd92l8eGHHw4geD7M1n4zUotbt27VXLNmzTSWlRCXLl2a9WOh8onzefZATZo00fjll18GELyOvfTSSzWOUguWle6ckALvveyCshFAQWmf6JwbCWBkmq9DlCmsWYqjctUta5YihDVLccPrgxyp9MR07713zpV6a++9HwdgHAAc7PPCYJ+gyV2h3ZHaTqi0EynlaYZ9gmGf9g0aNAgA91yIi1zVrEw4f+yxxzRndzGVnaTtxF275niqNb3tniJ79uzReOHC4nZX2Q8HCK4ffuyxxwIAGjVqpDk7iY2i52B1m6vzrIzude7cWXN2jyWJP//885RfL6N7QHJPGztiZ0cNba1TPGSzZu350HY5yJNg+35f2t5LmWLr1H5/Oc/KuR0o/W+BoiFO17S9e/fWuH794mksL774oubmzp0b+jFVVLpL9G5yzjUFgMR/N5fx+US5xpqlOGLdUtywZiluWLM5ku5NyGQAIxLxCAAvZeZwiLKGNUtxxLqluGHNUtywZnOkzHYs59zfUTxhp5Fzbh2AsQDuAzDJOXcNgCIAQ0r/Drllh2FtLEP+th3ld7/7ncZ2/wZhW7AuueQSjVesWJGZg6WMiGrNSvuAnVBpW1dkGN/Wqd2vxrZTiZo1a2pcr149jaU9QdaxB4CCgmSbq9R39+7dNScTf4HgXiUUjqjW7cHUqVMHQHCfkAcffFBjaVOxLSq2vlu1aqVxmzZtAARbEJ999lmNU7UjUm5FpWZtfckeYLatL1tkYRB7vrStWc2bNwcQvM6wrWOpFlngxPXsikrNpsuePy+//HKN5Vw8a9YszcVhEY/yrI41rJQP9cvwsRBlBGuW4oh1S3HDmqW4Yc1GS/YfFRARERERERmVXh0rTKlaq+zqVjY+7LDDAATX7JZ9GABg//79AIAbbrhBc126dEn5umvXrgUAXHDBBZpbvHixxmwToIqw9ZKqTcW2YNk1v1u2bAkA6Nu3r+ZsO1aqdcDt+vUNGjTQWFq77H44H3zwQcqvS3WsbBkgAGjfvj0AYN68eZpLdW605+66detqPGZMck8wqc/XXntNc7Nnzy7xvYgORs5Ntl5StWLbnG1bKevcJtcWQPKaQc7NQHA/MWkttNch27Zt0/iLL74o8f3j0EJDuWPf8y+88EKNpS7tKq/2mti2uUYJR0KIiIiIiChUkR8JsU+FZR1kILlD77XXXqs5O5lcJunar7c7qMrkMLuPgt192u6ZcOWVVwIAFi1apDk+CaZ0pXoqBySfWsgEMwDo1KmTxldddRWA5M7SQHBvEDvJfcGCBQCAGTNmaK5GjRoay2vI3gxAcK8HqXX71MVOco/qUxXKPvt0TZ4Av/POOyk/V86ptqZHjRqlsR1dllr+0Y9+pLlUT4qJKsqOKMveHR07dtScPSfLPk323GivE0466SSNjzjiCADBc6tdIESeTsuIIQCsXLlSY/lbks4MIDmxHkg9qkNVm73Otedi2X9m9erVmrM1bBdPkK6JKNQVR0KIiIiIiChUvAkhIiIiIqJQRb4dy05i/PnPf67xd7/7XQBA48aNNWfbqWR41U7yst9L2gjscJaduG4n6a5atarCx22Hd+W47LFEYRiMss+2A8rQvG0rtOvHS8uK3c/j9NNP17hPnz4AgkOs27dv13jhwoUl4i1btmhOWgcA4Oijjw68JgCcccYZGjdt2rTE8b/44osasx2r6jrmmGM0vvjiiwEk2/+A4DlV9gE555xzNNerVy+NbVvro48+CiC98y3Rgez1wNChQzW++eabAQSvHWy7lsT23GfZ6wRp8batqra1qmHDhgCCfzPz58/XeOfOnQCC5/HSFi4hAoL70tl21iVLlgAANmzYoDlbw926ddNY6sqea+1iNGFen3IkhIiIiIiIQsWbECIiIiIiClVk27Gkncm2o9g1kaW1xK6ekmqFCZuzbSy2DUXYlYp69uyp8dixYwEAEyZM0Ny6des0lvXtZegVCA7vfvrppwCCw2R29S2uC55fbDvK2WefrbGs5GZXt7A1t3HjRgDBVSzatGmj8eGHHw4gOYQPAB999JHGtiZt3Qs7NNuiRQsAQNu2bTV33HHHaSw1PXnyZM2xTqsu29pi22IHDhwIAOjfv7/m7D4IUtN2xR9bh3ZFt3vvvRcAW1UpM2zbq7RgAclWVNsybWtSznO27cqek/fu3auxrOhm26bsuVfeyrdB1QAAIABJREFUC6S9FQiutCXnctuileqahm1ZJGxdvvHGGwf9XFu3H374ocay19jw4cM1Z/dnmj59OoDkilvZxJEQIiIiIiIKFW9CiIiIiIgoVJFtx5JhTBnuB4Kr+8jwvm1HmTJlisayakXz5s01d+mll2osq7akWlELCA7lXnHFFQCAIUOGaM6uDiTDt7ZdxQ6ZFRUVAQgOd40bN67EsVJ+sJv+PfTQQxpLLdo627Fjh8YyDG9rJ1VN2XYA2/p13nnnaSztgNJ6ACRbsIDkqlz2+9vvK6+1e/duzdmhXapabNugPfdJTdhWwGeeeUbjefPmAQjWnm3d+u1vf6uxbKBFlAm25mQ1TCB5zrR1vGbNGo1lg9f//Oc/mqtdu7bGrVu31riwsBBA8O9D2maBZGuhtCUCwc0Me/ToEfg+QPA6xq6ERFQZts1v6tSpAILv77ZlUFbdks8DgtcqmWwP5EgIERERERGFKrIjIfJkoWPHjpqzExZlYve//vUvzdm9PWQSup1sa5/kyuRJ+/TNPgmWPR2A5L4N9mmHfZqdaiTETjyX17ATNu3TbsoPUh92RMKOxNWoUQNAsA7thF2ZpGgXTbALHNSsWRNAcJTOPu2zoyJyLHbCpX1dmXAmEysBYPHixRq///77AIDly5drjhPTqy5bOz/72c80vvvuuwEER0JSjdQNHjxYc/ap8Mcff6wxJ99SJtmatbFM9rajDL///e81tiMgwo5uyLkRSHYx2MnotmNDOi1sx8VRRx2lcZMmTQAA69ev15z9+5DrDP5tUCbJKODMmTM1Z0dCpOtH9oECgKefflrjTF6/ciSEiIiIiIhCxZsQIiIiIiIKVWTbsaT1yrZA2dYSYYeQTj31VI1lHeRjjz1Wc3YSzj//+U8AwPjx4zVnJ6fZ15XWLtsaY1vDpN3K5uzXS8uNHRJmO1bVYIfppR3LDq3bYXrZZ8a2W9mhfWltsa2CpbUISq3ZPUVs68u7774LAHjnnXc0Z+t/7dq1AIJ/M2wJqLrs737lypXl/jqp1WHDhmnO7pcUxjr0VDV98sknGts9Eo4//ngAwXOf7OUFJBftqFu3ruZs65at31T7MdnzsMT2PG3bZmUSvH0fsH8TPOfmn9IWMZBasteGYe6ZZKcLyMJN9vp5wYIFGs+ePTtjr1vmSIhzroVzbppzbrFzbpFzbnQi38A5N8U5tzzx3/plfS+iMLBmKW5YsxQ3rFmKI9ZttJSnHWs/gFu9950B9AZwg3OuM4AxAKZ67zsAmJr4N1EUsGYpblizFDesWYoj1m2ElNmO5b3fAGBDIt7tnFsCoBDAYABnJj7tKQBvAbg9UwcmQ1MLFy7UXKdOnTSWFau+973vac4ObUls9+B49tlnNb7//vsBANu3b9dcaUOfXL8+XnJVszJ0OmnSJM2df/75GksbgB2ul1Y/INnuZ4drLWnrS9UCAARXVZH9SV555RXN2XW+5XvYFa9SrX7FdoBw5Kpms8HWd8+ePQEAXbt21dxf//pXjcNsN6DMinrN2ram0aNHayz71Nj2U7sioaxYtXTpUs1t2bJF49LOv8KeMyW2dZ7q6+05n38T2ZWrupXzYvv27TVn90mSa9o333xTc3ZvuVWrVgEIXo9msnXLth/K9XMYK2JWaE6Ic641gO4AZgMoSPwyAWAjgIJSvmYkgJHpHyJR+lizFDesWYob1izFEes298p9E+KcqwPgOQA3e+932add3nvvnEv5yNR7Pw7AuMT3KPdjVXmCYPcB6dOnj8YyccZOFrdkBOTFF1/U3MMPP6yxjIDwSW/+CrtmRVFRkcaDBg3S+MQTTwQAnHnmmZqzo3uyfvzWrVs1Z0c35MmcnSS5bNkyje3kSi58EE+5qtlMshNvR44sfq+29Wh34aX4i2rN2vf2JUuWaCx72nTo0EFzjRs31lgmntvJ7Ha36WzgdUj4wq5bWXygXbt2mrOjD4WFhQCS50wAOOecczSWa13bvTNlyhSNn3nmGQDA5s2bNWcXQ7I1JiN/PXr00Jzdy0kWFJk1a5bm7D58mVSuJXqdc9VR/Mua6L1/PpHe5Jxrmvh4UwCbS/t6orCxZiluWLMUN6xZiiPWbXSUZ3UsB+BJAEu89w+YD00GMCIRjwDwUuYPj6jiWLMUN6xZihvWLMUR6zZaXFnDgM65PgDeBrAAgMx8+SmKe+gmAWgJoAjAEO/99pTfJPm9KjzmaPcB+fGPf6zxRRddBCA4ccaucyyTe5544gnN2daWMCbcVNJc732Psj+NDpTrmi3H8Wls976pWbNmic+1kxglthPQIjaMz5pNU9RrtiLsxMtXX30VAPD2229r7rrrrtM4Audh1mya4lqzcs6VicAA0KxZM41lfxG7qE22z7N2Ynqqie0HYM1WQph1m2oRGvs+L4vVAEDLli0BJNuygWBra/fu3QEEW7htm+vcuXMBpN7vAwC6dOmisbyGPf/aCfGyeMjMmTM1JwvjpMt771Lly7M61kwAKb8YQL/KHBRRNrBmKW5YsxQ3rFmKI9ZttJRrTggREREREVGmVGiJ3lywKwE89thjGsvQkR2O2rNnj8arV68GAOzcuVNzERj6JwoMsadqtyKKG9tOMmDAgBIf/8tf/qIxz8OUS1J/dhVC23olK2GF0eoqfzf2tWyLLt8T4s3+Xm2NCbu6lW3dSkVqpbQWbmndkrYuINnCBQSvhWWlrvfff19zdiVaaekK5W8g669ARERERERklDkxPaMvlsHJZ6nuGm0uD3Yd5eSzCMj1JN+YYc1GQC5qtn79+hr/+9//1lie3g0cOFBzdh37CGDNRkAuatZeL8Ts2oE1GxG8Pii/0iamcySEiIiIiIhCxZsQIiIiIiIKVeQnppcmVRtZxPZMICKqEvr376+xXZt+2rRpAIKTIomioBz7cRBRlnEkhIiIiIiIQsWbECIiIiIiClVs27GIiCgaPvnkE41t69WHH34IgPsdEBFRSRwJISIiIiKiUHEkhIiIKuW9997TuFOnTjk8EiIiiguOhBARERERUah4E0JERERERKEKux1rK4C9if/mm0bI7M/VquxPoRCwZsuPNRsNWwEUIfO/3yhgzeYn1mz5sWajI1+vD0KrWRf2Jj3OuTne+x6hvmgI8vXnovz93ebrz0XF8vH3m48/EyXl4+83H38mSsrH32+YPxPbsYiIiIiIKFS8CSEiIiIiolDl4iZkXA5eMwz5+nNR/v5u8/XnomL5+PvNx5+JkvLx95uPPxMl5ePvN7SfKfQ5IUREREREVLWxHYuIiIiIiELFmxAiIiIiIgpVqDchzrn+zrmlzrkVzrkxYb52pjjnWjjnpjnnFjvnFjnnRifyDZxzU5xzyxP/rZ/rY6XKY81S3ORDzQKs26qENUtxw5rN0OuHNSfEOVcNwDIA5wBYB+B9AMO894tDOYAMcc41BdDUe/+Bc64ugLkALgJwFYDt3vv7EgVZ33t/ew4PlSqJNUtxky81C7BuqwrWLMUNazZzwhwJOQnACu/9Su/9VwD+AWBwiK+fEd77Dd77DxLxbgBLABSi+Gd5KvFpT6H4l0jxxpqluMmLmgVYt1UIa5bihjWbIWHehBQCWGv+vS6Riy3nXGsA3QHMBlDgvd+Q+NBGAAU5OizKHNYsxU3e1SzAus1zrFmKG9ZshnBiepqcc3UAPAfgZu/9LvsxX9zjxrWPKVJYsxRHrFuKG9YsxU2uarZSNyEVnJjzKYAW5t/NE7nYcc5VR/Eva6L3/vlEelOit0567Dbn6viodKxZ1mwcVaBu86ZmAdZtnLFmWbNxw5oNv2bTvglJTMx5FMAAAJ0BDHPOdT7Il7wPoINzro1zrgaAoQAmp/v6ueKccwCeBLDEe/+A+dBkACMS8QgAL4V9bHRwrFnWbBxVsG7zomYB1m2csWZZs3HDms1Nzaa9OpZz7mQAv/Den5f49x0A4L2/9yBfwyHI8tvqvW+c64PIJ6zZrGPNZkFF65Y1WyGs2SxgzWYVazYL0qjZ8wG8Gt4Rxpv33qXKV6Ydq1wTc5xzI51zc5xzcyrxWlVRUa4PIA+xZrOLNZsdZdYtazZtrNnsYM1mD2s2Oyp0fQDgV2EdWD47NNsv4L0fB2AcwKcdFA+sWYob1izFDWuW4oh1m1mVGQnJq4k5VCWwZimOWLcUN6xZihvWbA5U5iYkbybmUJXBmqU4Yt1S3LBmKW5YszmQdjuW936/c+5GAG8AqAZgvPd+UcaOLEKKFw8A0p3ET9FQlWqW8gfrluKGNUtxw5rNjbRXx0rrxWLaP5ejm5C53vseYb4glRTXms0R1mwEsGYrhDUbAazZCmHNRgTrtvyysToWERERERFRhWV9daw4qVOnjsbNmjXTuHv37gCAzZuTG0bOmjVL4y+//DKEoyMiIiIiyg8cCSEiIiIiolDxJoSIiIiIiEIV+XYsmRQOZHZi+CGHFN9/tW3bVnP9+/fX+Oyzz9a4Q4cOAIBPP00uGf3b3/5W42nTpgEA9u/fn7HjIyIiotzr1q2bxk8//bTGM2bMAADcdNNNmvv222/DOzDKG1V1FVaOhBARERERUagiPxKSybvC2rVrazxq1CgAwMCBA1N+/IsvvtB49+7dAID69etrrnfv3hrPnDkTAEdCqHLkSUhpo39V7QkJkWX/LgT/JihbbL3dfvvtGrdu3VrjXbt2lfhcolSqVasGADj00ORlt72mPPLIIwEA33zzjeb27t1bIrY5+7lxxZEQIiIiIiIKFW9CiIiIiIgoVJFvx6qsww47TONHHnlE4+9+97sAgM8//1xzr732Wsq4QYMGAIDTTz9dc/Xq1dO4Zs2aAIB9+/Zl6rCpiqhevbrGffr0AQB06dJFc2vXrtV4wYIFAICtW7dq7uuvv9bYtqZI3n6cKAz2nNu8eXMAQKdOnTR3zDHHaHzUUUdpLC2wmzZt0pxtN5C/C1v/48eP17ioqAgAW7QoM2rVqqWxbb+uUaOGxlJznIxOZWnYsCEA4He/+53m7DWl1Ju0+AHJqQAAMGfOHADApEmTNDd//nyNd+7cCQD46quvNBeHuuRICBERERERhYo3IUREREREFKq8bceSlQh+9KMfaW7YsGEaSxvMxo0bNWeHyVauXKlx3bp1ASRbCwCgUaNGGsuqWp999llGjp3ym9QmAHz/+9/X+Ic//CGAYDvLf/7zH42ltWr16tWakz1sAOCEE07QWIZ0//jHP2rOrqpBlAmyKlD37t0199hjj2ncvn17AMG2QxvbdsE9e/YE/gsATZo00bhOnTolPr5582aN//SnP5X4nkTpsqtgNWvWTGO7EtYLL7wAgC2AlJo918l1on3Ptu2oUle2DdC2/Uu+R48eJb6nZdu55s6dq7G9bpDr3jfffFNzGzZs0DjMeuZICBERERERhSpvR0LatWsHILi+t0wgB5ITHh999FHNLV26VGM7oUdie6doJw/LSAlRediJubfccovG8qRXnq4BwAMPPKCxPPW1T+Lsk5ILLrhAY3lCMnHiRM1xJIQyrWvXrgCAV199VXMFBQUayyTJLVu2aG7btm0a20noUt92lNmOhMjTueXLl2tu8uTJGnOfJsqkO+64Q2M7Gd3W2aJFi0I9JooXWysygrtu3TrNFRYWaizXkfZr7KiG1KD9GjsScsghxWMKdhSjW7duGttrWrn+lYUVgOCeeZ988kkZP1nmcCSEiIiIiIhCxZsQIiIiIiIKVV61Y9kJvTJJsX79+pqzbSyy/8ITTzyhObsmvSXDY3boyra2tGrVCgCwbNkyzXGiGh1IhksHDBigOTuc+oc//AEA8NBDD2ku1STbVHUMAEcccYTG0trFOqRMs+fZP//5zwCCLVh2H4/7778fQHACpG3Hsq0H8j0uu+wyzdnFQKSl66abbtKcbW1grVMmyGTiSy+9NOXH7XWCbdEmOpA9J+3YsQMAcM8992hu1KhRGh9//PEAku/dQHARG2nHkusIIHgtUBb7uTI1oW3btpr7n//5H41vuOEGAOHsM1LmSIhzbrxzbrNzbqHJNXDOTXHOLU/8t/7BvgdRmFizFEesW4ob1izFDWs2WsrTjjUBQP8DcmMATPXedwAwNfFvoqiYANYsxc8EsG4pXiaANUvxMgGs2cgosx3Lez/DOdf6gPRgAGcm4qcAvAXgduSAHa6yQ1t9+vQBEByCssOod955JwDg888/L/M1ZEhq4cL/3969h0tVX/cff3+rqFWpohIERPEWUhJj8FG8IRAv0Rj7qIkxNU2kjQZNYxRrjUbzaIz2SR7b2l+aagxRI30kIglWqCZBa7yhICDiDRAUQZCLIiiImsRk//44s9ZZA3M8t5k9s+d8Xv+w+J5zZvY8s84+s/d3fdfXL5x57733PLZOWTNmzPCxjjyu1Eaj5uy227b8un3sYx/zsVji19F9DuIU7/vvv+9xnMa1/NTeNcXRqHm7pVgiZdP5cb+O0047zePZs2cDbU/rx/O3lRO2tU/OzTffDKjstZEUJWc749RTTwXKu2lGc+bM8fjtt9/O5ZikeuqVs/b5M56/5s6d67F1BYwlUvHzq3UajB2z4mda+974Nz/Gcf8RK32N59+4z5iVfsXPF7XS1TUh/bIss2LINUC/tr4xpTQWGNvF5xGpFuWsFFGH8lY5Kw1EOStFo88HddLthelZlmUppTZvR2VZNh4YD/Bh39dZdtU3cuRIH7vmmms8trvO0YIFCzy+++67O/2c8aoz3g20fR9ee+01H5syZYrH2sG3sdQrZ028IxH7zHflrkPsAx4f1+5A//73v+/KIUoD+rC8rXXORnGPJFtYPn78eB+Ld4rbW9gY7zYff/zxQPks80033eSxnV81+1EcjZKznREXDlcyefJkj/NYuCv5qvXng1glE2dCvva1rwHlu6jHzwSLFi0CYObMmT4W91myhh+xWUf8HDx06FCPL7roIgAGDx7sY7GSIs+ZkK626F2bUuoPUPr39Xa+X6TelLNSRMpbKRrlrBSNcrZOunoRMg0YU4rHAFOrczgiNaOclSJS3krRKGelaJSzddJuOVZK6U5aFuzskVJaCVwN/BCYnFI6B1gOnFnLg6xk9913B+BHP/qRj8XpJPPOO+94fPbZZ3vclTKVOE02YMAAj21K66qrrqr4+FOntuSzpm7z0ag5a6Urs2bN8rHevXt7bP3p494JlcTFal/4whe2+nmAO+64A1DOFUmj5m3p2Dw+8MADPbYF6RMnTvSxtvZbMrHPfdzzw/bPsaYhUF7iqjKsxtPIOdsZsSwwllqbmHtxMbr9Xig3i6PeORv/Ji9btsxjO9ftvffePhY/v/73f/83AL/97W99LC5Sr/S3Pp5r44L4ww47bKvnshIsaP1cEh+/VjrSHeusNr50XJWPRaQqlLNSRMpbKRrlrBSNcraxdLUcS0REREREpEu63R0rT7Hc5JxzzgFgyJAhPlZpTxDbewHKu2N1hnUdOvLII30sltHsuOOOQHmZwnXXXeex7R+ybt06H9P0bc9j06X33nuvj511VutNGeuy9swzz/hYpdKWWHZ4zDHHeBx7flvJQNxzIf5+2O9SHNu8efNWxxrzVDnbc8Vp/X322cdj6+4Wy6YqiXlmezgBnHfeeR4/+OCDADz55JM+ppyTPNi+CVBelmJih8tYQiPSHZs2bfL4scceA6B///4+Zl0uAR5++GGgvBywvXLr+PnhjTfe8PgXv/gFAKeccoqP2RIHgKOPPhoo7wRXK5oJERERERGRXDX8TEi8gxYX0VxyySVA5bsW0LpgctKkSR1+rni3b7fddvP48MMPB2D48OE+Fmdl7OfinejYf9n6M9uVLugOX08Wd0l/4oknPLY7xLHpQZy9s2YHxx57rI/Fu9Ixf+0OR9ydfd999/V4+vTpACxZssTH4t0+6w+uPBUoP9/ZzuYAK1as2Op74znb8mennXbysQsvvLDi49rscXuNGUSqbdiwYR7H86iJM33PPvtsLsckzS/+zb3rrruA8pm2+Pff9gHparOZ+Lfc9ieJ1Tmxkufiiy8G4J577vEx27G92jQTIiIiIiIiudJFiIiIiIiI5Krhy7Gi2FM+LqIxcbrJpo4GDhzoY6tWrfLYFvfYonKA0047zePPfOYzHtvi3vXr1/vY6tWrPY4LiUycMrPnUmmLQPlisZkzZ3pspVPf/OY3fSzmrOVfXExmTRMA3n33XY+fe+65sp8BeOqppzxetGgRUL4YPe5to/1FJIrlVLHEzxYwvvjiiz5mpX7QmkexgUI8X/7qV7/yuL3F7SK18g//8A8eWzlhPAf+7Gc/8zguJtbfdOmOmD9r1qwB4P777/exmIPVLFN97733gPK9Q+J53ZYQWLMcKG+YU02aCRERERERkVzpIkRERERERHJVqHKs2H2qktiVpU+fPgCce+65PjZy5EiPredy7GJ10kknebzHHnt4bB1g4hb2ca8G66YRp85Wrlzp8dKlSwFN3crWrAsVwM9//nOgvERr++2399g6tp1xxhk+Fku7fvKTn2z1WPHrKrGSroqlerE7lnVcu+mmm3zMurhA6z4i8edjCezUqVM9Vn5KnmJnzbh3jYnlrbfffrvHlfZuEukuO//FvKvUabAa7HHjZ+rYFc7O0bGM1kq847FWg2ZCREREREQkV7oIERERERGRXBWqHGvKlCken3322QD07t274vdaGctRRx3lY3Gzw223bXnpcerLyqagvJOQbXz42c9+tuJjmXfeecfj7373uxXHRdpipVnz58+v+HUrg4kbDFnnNoD/+q//8jhugiTSXdZNBcq7t1g5ayxt2WWXXTy2LoaxrCBuehXPv1YaEMsOVMIqtbLnnnt6vOuuu2719VdffdXjN954I5djEmnr/Gfn0K6WaMXSK9sQOW52HB/LSg5j6WF83mrSTIiIiIiIiOSq4WdC4tXZjBkzPD7zzDOB8gWRH/nIR7b6+XiHzhb2QutdkFmzZvlYvMMX79B98YtfBGD48OE+Fu9A213n2267zcemTZvmsRZcSjXY3eiYe3FWxGbsRKot3hG7+uqrPf71r38NwBFHHOFj8Ty81157AeXnTuuHD5XPjZr9kDx89atf9bhS05s777zT42ru0SDSFTYTEfcGixUPFscZC6v4gfIZ6mHDhgHlDZjiz9n5vlevXhW/Xk2aCRERERERkVzpIkRERERERHLV8OVYUZwStdKp2Md41KhRHh955JEAfOITn/CxuCDSeh4//fTTPrZ69WqPBw0a5PHxxx8PlPfHjyUDy5YtA+D666/3MS0MllqJiyhjaUvci0GkVjZs2ODxAw88AMDjjz/uY5/61Kc8vvTSS4HycpeYs3buBJ0zJR+2H8L5559f8ev2OePWW2/N7ZhEOqp///4ex+YKb731FlDeRCHuQxb3Z7IF6bG0q5L29uarhnZnQlJKg1JKD6WUFqSUXkgpXVQa3y2l9EBKaUnp3z41P1qRDlDOStEoZ6VolLNSRMrbxtKRcqwPgEuyLBsKHAF8M6U0FLgceDDLsgOBB0v/F2kEylkpGuWsFI1yVopIedtA2i3HyrJsNbC6FG9KKS0EBgKnAqNL3zYBeBi4rCZHWfm4AFi1apWPTZo0aas4dhKKZSzWKSt+PZYRnHTSSR5baVbs5PL22297fOGFFwKwdu3arrwUqbJGzdnusqnROMXaXqeh7upqT3LpnCLlbMyDSjk3cOBAjw844ACgvNQqdg5cv359LQ5RclCknI2sI1C/fv0qfv3NN98E1G2wWRU1b03seHXyySd7bJ1g77vvPh9bsWKFx3E5gXXHsv30oPy8bqXdcYlCrbq8dmpNSEppMDAMeBLoV3ozAdYAFX+jU0pjgbFdP0SRrlPOStEoZ6VolLNSRMrb+uvwRUhKaWdgCjAuy7KNW9whzVJKFW+TZlk2Hhhfeoya3kqtdKc27vQbY1tcefDBB/vYiBEjPD7kkEM8toVq8c7IhAkTPH700UcB7QfSaIqQs51hCyo3b97sYxs3bqzX4UgNFC1n7a6c7ZwOrfsqAfTt2xeAl19+2cf+53/+x2OdM4uvaDlrzWziotuYh1OmTAHK98aR5lO0vLXPt3Em5NOf/rTHtkjd9mYCmDdvnsdxhtp+B2IlUGSfK2bOnLnV81dbh1r0ppR60fJmTcyy7O7S8NqUUv/S1/sDmruUhqGclaJRzkrRKGeliJS3jaMj3bEScCuwMMuyG8KXpgFjSvEYYGr1D0+k85SzUjTKWSka5awUkfK2sXSkHOto4KvAcyml+aWxK4AfApNTSucAy4Eza3OItWF7hsQF5tG6des8fvXVVwG4+eabfezhhx/2OC4UlobQlDlr5QExZ22RJZRP01ZrzwUtRs9NYXK2V69eHlszj29/+9s+NnLkSI8tZydPnuxjWvDbNAqTs9Ho0aOB8nNb3IPszjvvzPuQJF+FzFvL17jYfOnSpR7vt99+QGt+Q/mygp133tnjXXbZBWgt8QZ49913Pb7xxhsBWLly5VbPX20d6Y41A0htfPm46h6OSPcpZ6VolLNSNMpZKSLlbWPp0JoQERERERGRaulUi95mYt0wFi9e7GPXXHONx7atPbT2So77gKhzhuTNpkPjFKrtd7NlbCUvKqeSaoslgNdeey0Ao0aN8rFYFjh79mwAJk6c6GOx9EUkb7Y3gpVkQ/l+S8uXL8/9mEQ6KpZj/9M//ZPH48aNA+D000/3MetOCLDjjjt6bJ3hYglWPEdb99c8PudqJkRERERERHLVY2dCTOwPHhejx1ikEdhdibhYzHak3jJ+4403AM2ESHXEHvr777+/x7bwMe68GxeeX3nllVuNidTT2LEt+8ydf/75PhbPkzpnSlHE6pzrr78eKN9H7Nxzz/U4zmrYjujPPfecj91+++0er1+/vurH2hbNhIiIiIiISK50ESIiIiIiIrn0YaouAAAePklEQVTq8eVYIkVh06lz5871sSFDhngcy7HmzJkDlC++FKm2jRs3ArDddtv52G233ebxrFmzgPKyV5FGoJyUZrJhwwYAfvKTn/hYLIONC9NnzJgBwIsvvuhjcZF6niWJmgkREREREZFc6SJERERERERylfKcdkkpqe1Exz2VZdmh9T6Inq4RczbuE7Lrrrt6bNOxULcOL8rZBpBnzlpXrD/+8Y8+VrAyF+VsA2jE82wDU842COVtx2VZVnGXes2EiIiIiIhIrrQwXaRg4p3mPPt5i2zJ+s2LiIh0lmZCREREREQkV7oIERERERGRXOVdjrUO2Fz6t9nsQXVf1z5VfCzpOuVsxylnG8M6YDnVf38bgXK2OSlnO0452zia9fNBbjmba3csgJTS3Gbs7NCsr0ua971t1tclLZrx/W3G1yStmvH9bcbXJK2a8f3N8zWpHEtERERERHKlixAREREREclVPS5CxtfhOfPQrK9Lmve9bdbXJS2a8f1txtckrZrx/W3G1yStmvH9ze015b4mREREREREejaVY4mIiIiISK50ESIiIiIiIrnK9SIkpXRSSunFlNJLKaXL83zuakkpDUopPZRSWpBSeiGldFFpfLeU0gMppSWlf/vU+1il+5SzUjTNkLOgvO1JlLNSNMrZKj1/XmtCUkrbAIuBE4CVwBzgrCzLFuRyAFWSUuoP9M+ybF5KqTfwFHAa8PfA+izLflhKyD5Zll1Wx0OVblLOStE0S86C8ranUM5K0ShnqyfPmZDhwEtZli3NsuwPwCTg1ByfvyqyLFudZdm8UrwJWAgMpOW1TCh92wRa3kQpNuWsFE1T5Cwob3sQ5awUjXK2SvK8CBkIrAj/X1kaK6yU0mBgGPAk0C/LstWlL60B+tXpsKR6lLNSNE2Xs6C8bXLKWSka5WyVaGF6F6WUdgamAOOyLNsYv5a11Lip97E0FOWsFJHyVopGOStFU6+c7dZFSCcX5rwGDAr/36s0VjgppV60vFkTsyy7uzS8tlRbZzV2r9fr+KRtylnlbBF1Im+bJmdBeVtkylnlbNEoZ/PP2S5fhJQW5twIfBYYCpyVUhr6IT8yBzgwpbRvSmk74G+BaV19/npJKSXgVmBhlmU3hC9NA8aU4jHA1LyPTT6cclY5W0SdzNumyFlQ3haZclY5WzTK2frkbJe7Y6WUjgS+l2XZiaX/fwcgy7IffMjPaAqy49ZlWda33gfRTJSzNaecrYHO5q1ytlOUszWgnK0p5WwNdCFnTwbuy+8Iiy3LslRpvDvlWB1amJNSGptSmptSmtuN5+qJltf7AJqQcra2lLO10W7eKme7TDlbG8rZ2lHO1kanPh8A38/rwJrZtrV+gizLxgPjQXc7pBiUs1I0ylkpGuWsFJHytrq6MxPSVAtzpEdQzkoRKW+laJSzUjTK2TrozkVI0yzMkR5DOStFpLyVolHOStEoZ+ugy+VYWZZ9kFK6AJgObAPclmXZC1U7sgbS0jwAdtppJx/bc889PV66dCkAf/7zn/M9MOmUnpSz0jyUt1I0ylkpGuVsfXS5O1aXnqyg9XN1ugh5KsuyQ6v5gNJ5Rc3ZOlHONgDlbKcoZxuAcrZTlLMNQnnbcbXojiUiIiIiItJpNe+OVVQ777yzx9/61rcAOPfcc33s8ccf9/j8888H4N13383p6EREREREikszISIiIiIikitdhIiIiIiISK5UjhXssMMOHl933XUen3POOQD8/ve/97H77rvP4/feey+HoxMRaXzWyANgm222KfsXIDZDiXGlxh7tfV1kS5Z/f/EXle+xWh7l2ZRHpCviubQ9Rc1nzYSIiIiIiEiuevxMSLxDd/rpp3t89tlne2yL1FetWuVjjzzyiMdFvQKVYrG7Ittvv72P7bPPPh5/9KMf9Xi77bYD4Pnnn/cxayUN8Mc//rFmxyk9T5xFjufRE044AShvb/5Xf/VXFeNdd90VaM1dgPfff9/jadNa9g27/vrrfeztt9/u9rFL8cVZD2uf/8lPftLH9t57b4+XLFkCwOLFi33snXfe8fgPf/gDAH/605987IMPPvBYM3JSC9tu2/JxfMiQIT727W9/2+PDDjsMKP/7v+OOO3psnw/i59GYt6+//rrH8+bNA+COO+7wsVmzZnkcq35qTTMhIiIiIiKSK12EiIiIiIhIrnpsOZZNXR1++OE+NnbsWI9tShZgwYIFAPzbv/2bj73xxhu1PkQRdtttN4+/9KUvAXDMMcf42IABAyr+3FtvvQWUl6tMnjzZ44ceegjQ3jbSPb169QJgzJgxPva9733P47jfkonlLLGMxh7LyhKgvLSgf//+ADz77LM+9qtf/crjWD4jPcvuu+/u8bXXXgvA0Ucf7WOx/HTZsmUAvPzyyz4Wy1Y+/vGPA635CLB69WqPn3jiCY/nzJkDwJo1ayo+l5V7xxLD+Fh5lr1IY7PPpEcddZSPfeYzn/HYcjyeM2PemraWB/Tt29fjgw46CIAvfvGLPjZ9+nSP7Xwey2FrRTMhIiIiIiKSK12EiIiIiIhIrnpsOZZNuf7rv/6rj/Xr18/jRx991GObporTVeqQIbUSu7r87Gc/89hKW37zm9/42E033eRxnOa3qdtRo0b52BFHHOHx2rVrAXj66ad9TOUs0hGxd/1+++0HwMUXX+xjsTTGSgPiXkqxlDWWsViZQeyYNXjwYI+tTCt+XefhniuWpRxyyCEejx49GmjtkgWwadMmj60cK5anWKkfwL777guU59mnPvUpj0eMGFHxcU0sJ7RjiKVdGzZs8HjkyJEAvPrqqz6mnO6Z7O9vLPezsmmA/fffHyjPn/nz53u8fPlyoLyLq3UcBDjppJM8PuWUUwDYZZddfCyWftl5d9GiRV14JZ2jmRAREREREclVj5oJiXc7br31VgAOOOAAH4u7oN9yyy0eL1y4ECi/AtXeIFJtw4cPB+CXv/ylj8W7fdYzfMqUKT4WGyhEr7zyCgArVqzwsbgIzWZFrOkCaJG6dEzcE8RmQAYNGuRjcabE9l9oazH5jBkztvpe64cPcOmll3psd/VeeuklH9N5uOeKe3ydeOKJHlszjziza+dDaP07b3eOoXxWxGZAYlOQuB9DrJiwO8nxPG0zKdD6u/KXf/mXPhb3zKnU9Ob73/++x5UWHktzshmwOPswbtw4j21Wbf369T4WF47bbHPMmbinSNwnxCokYl6/+eabHscZ6lrTTIiIiIiIiORKFyEiIiIiIpKrpi/H6tOnj8dTp0712Bb//u53v/OxOCUaF4pZmYCmRqUa4tT9oYce6vGkSZOA8p7yZ511lsePPPII0LESFJvajVOscTrWFkTGMoRYniASxZy1RY0AX/jCF4DyhbexrM8aH/z4xz/2McvjLb/XniMulty8ebPHtv+C9mgSKD9PxrLqSue+uEfSPffcA5TvoRQXg1sZV8z5uNg8llZZHPf7iPl73HHHAfCd73zHx2Lpov38eeed52M333yzx3GRsfQMsYxw3bp1Hlu+xnK+2DzB8jX+fMy1888/32MrbY2/A9/4xjc8tn3G8tDuTEhK6baU0usppefD2G4ppQdSSktK//b5sMcQyZNyVopIeStFo5yVolHONpaOlGPdDpy0xdjlwINZlh0IPFj6v0ijuB3lrBTP7ShvpVhuRzkrxXI7ytmG0W45VpZlj6aUBm8xfCowuhRPAB4GLqvicXWbdQWI+yjErivWreXCCy/0sZUrV3psU/+gvt1F06g5ayUrX/nKV3zsu9/9rsc2nfrlL3/Zx2LpSlc6AcWpWdtnBGDYsGEAHHvssT42YcIEj5Xz+WvUvIXy/Tr+/d//3WPrIBRLVWMZzOzZs8v+hdbyVijPT8v/WNoSy7ysdCX2vpf6qmfOxlLroUOHbvX1WMoUO19aOV9n9kWKXQjjnjcmnptjueDSpUuB8r2dYln4wQcfDJR3KYr5rXKs6mvk8+yW4t9hO8cOGDDAx2LHSzuvxo5YsXTW9saD1nPslVde6WP/93//V63D7pSurgnpl2WZ7Yy2BujX1jemlMYCY7v4PCLVopyVIupQ3ipnpYEoZ6Vo9PmgTrq9MD3Lsiyl1OYt2izLxgPjAT7s+6oh9qe33R/jLpGxv/znP/95oHX3VCi/6lT/+eaVZ87GBY1XXHEFAP/4j//oY3Hh2SWXXAJ0f/YjiosrbTE6tN4Vif3177jjDo81E9J4Pixva3WetYWP8e7tXnvtFZ8XKN9D6eGHH/b4rrvuAsqbIrTV4MMeKy6WjP3qbcfggw46yMdmzpzpcWfubEs+apmzcYFubGBgM76x6UH8enfPbZ05J9tzxRmNeMfZZkLi34k4qxP3cZJ8NNJn2shyKeZKnN2w/Wnifni9e/f2OC42tz3H7PwM9fvM29UWvWtTSv0BSv++3s73i9SbclaKSHkrRaOclaJRztZJVy9CpgFjSvEYYOqHfK9II1DOShEpb6VolLNSNMrZOmm3HCuldCctC3b2SCmtBK4GfghMTimdAywHzqzlQXZUXNz1z//8zwBs2rTJx+Ii9BUrVgBtl2DF0i6VZhVLvXM25s7o0aM9tj7dcWFu7M39+OOPA9UthYrTsccff7zHVsoQF/4qz+ur3nlrYpnLtGnTgPISkZgntvD22muv9bF7773XYysB6EhOVyrHsvM0tDYWib9Td95551bPJfmpZ86uX7/e45hfds6L57ZYwmLn5zzPd/G54u+XicevPK6tRjnPdpbl0OLFi33MPjMAjBo1CihvbBBzyT4TA/zyl78EGqPsuiPdsc5q40vHVflYRKpCOStFpLyVolHOStEoZxtLV8uxREREREREuqTb3bEayemnn+7xgQceCJRPV8VOEzYlu9122/lYnL7dZpttPLbOGm11dRGJbO8EKN8HxHLq6quv9rGYn93t7hPLwCyvYwniRz7yEY9tajeWzqi7UM8Ve8vHafsjjzwSKO+yFkukrE/9woULfSzuqdCVkpe4R1Pcc8H2Cfn0pz/tYyNGjPDY9oJQWWHP8P7773sc/zZbrsaylF122cXj1atbOrHGPKt1zuywww4eDx8+3GM758bSsueffx6RtsR9ah577DGPLYfj54B4rp47d67HjVCGZTQTIiIiIiIiuSr8TEhcjP61r33NY1v8u2jRIh+L/ZNtt9UDDjjAxwYNGuRx3759PX7mmWeA8v7eccGP7iALtC5+jHdq454KtifIrFmzfKzSHbh4JyOq9L1xxs72dIDWPRXGjBlT8XttV2tboNbW40tzs1wbMmSIj8XzqM0Ox13ML730Uo/trm01Z4njHepXXnllq2ONzRbOOOMMj3/7299W/VikccW7uXEmz8QZ6bifgs2uxQYIMb/tcatxPrTfH9u3DGDw4MEe26zha6+9ttXzi1QS8zJ+Dl27di1QPgMYP9NecMEFHtueZXFWpV40EyIiIiIiIrnSRYiIiIiIiOSqsOVYNjV/+OGH+9iAAQM8XrNmDdA6RQWtJVjQWq5yzDHH+JgtfNzye628Jvant6n/GMcpXel5KpVRxalTWzB58skn+5jtwxC/Nz5OpZKBWFYVGyvEn7NywpjHccHwuHHjgPI9S6TnsZz52Mc+5mMxZ6w0KjYwuOeeezyuRelT/J2JC9Ot7DXm/yc+8QmPrfRF5Vg9T8xZK4uN+3EMGzbM4w0bNgCwcuVKH7PF6tBaDhUXk8eS63fffddj+/2IORt/7ogjjgDgBz/4gY/Fslk7v1tTBWgt2xVpz8aNGz22z6GxDHGPPfbw2PYpg9ZGDV//+td9rF7LCjQTIiIiIiIiudJFiIiIiIiI5Kqw5VjWDSN2/7FpWIDf/OY3QHkf5UrTTbErRZz6j49l3SxOPPFEH4u99G0q+I477vAxlQT0PDY1P3v2bB+bP3++x1Y6eMkll/jYl770JY9tH5v//d//9bEnn3zSYyvHinkcy6liOZZNw8bSlTjNH59Dei4rI4kdVWJZ6aZNmwC46qqrKn69lscE5XvbWHlNzPPY3UXd3XqWWPYUYxP/Bu++++4e9+vXDyjPrXietI6GsdQ1lmC9/PLLHts5O+5ZYo8P8K1vfQuAgQMH+ljMU+veOWXKFB9Tdyz5MHHPpujnP/85AE888YSPXXPNNR7HDnF/93d/B8Att9ziY/Hn8qSZEBERERERyZUuQkREREREJFeFLcfae++9ATj66KN9zLpeAMycOROAZcuW+VgsY1m1ahVQXloQ4zg9axvCfPnLX/axv/7rv/b42GOPBco3flM5Vs8Vu66MHTvWY8uTmLMrVqzw+PHHHwdg4cKFPrZ582aP2ys3se5AAMOHDwfKSwqeffbZio8rYt0Eofw8+uqrrwKt58s8xO5BVs4CrfkdS7AmTpzosc65PYt1+IHyv9d2noz5EM99dn6N58tDDjnEYyvHmjdvno/Z7wGUdxm0Ety2ygKthDBuwBlLaG+44QYAlixZUvE1ihgrQ41dXGNpqpUJvvjiiz4WN0aeMWOGx/vuuy8AP/7xj33ssMMO8zjPkkDNhIiIiIiISK4KOxNy8MEHA+ULyOMdPIvjHYjIrvTiXYt4NyX2HT/99NOB8l76caHaXXfdBZTfDZGeK95FiDlpiw/vvvvuij/X3YW1vXv39vhzn/scUD7799Of/tTjevUEl8ZiORdn7+IiWzt/xvNsrey4444ATJgwwcfiHWq76xfvSk+ePNljzYT0LPH9jnGlmZDYlMP2Dot3keN5evr06UD57GCc/Yjs9yL+fsTvtX0c4uPff//9HlvFRlufU0SMfSbt37+/jy1fvtxjy7v4OSLm8OWXX+7xL37xCwA++tGP+phVF0F5BVGtaSZERERERERypYsQERERERHJVaHKseL0qW1NH8d23nlnjwcMGACUT8PGchWbhrIFOlC+2NzKvaC1n3jcsyH2X7Ze4ervLR1Rq/0MjjvuOI/79u0LwOrVq33sd7/7Xc2PQYop7pcUS/UOOOAAAPbbbz8fe/PNNz3uSh5VOo8D3HbbbQCcfPLJPhZ74tu53MpjAdavX9/p55fmEPPQSqygdc8bWxQO5aXUc+fOBcr3BYvnSSuNaiu3Y/5aHJsp7Lnnnh7bYzz99NM+Zvs5QPnnE5Et2R51AKeeeipQee8wqJyvcSz+nDVbis0dTjnlFI9vvPHGNh+z2tqdCUkpDUopPZRSWpBSeiGldFFpfLeU0gMppSWlf/u091gieVDOStEoZ6VolLNSRMrbxtKRcqwPgEuyLBsKHAF8M6U0FLgceDDLsgOBB0v/F2kEylkpGuWsFI1yVopIedtA2i3HyrJsNbC6FG9KKS0EBgKnAqNL3zYBeBi4rCZH2XosHi9evBgo3wchTrleccUVQHl/8EqlV3HKNnbHsp7LABdccAFQXs4Sp8GksTRSztZazNmvfOUrHlsZy2OPPeZjmzZtyu/ApFPqnbMxN+I5deDAgUB5Z5VvfOMbHltJS1vT9pafsVzl7LPP9vjSSy/12EoIYwlWLJmxMi0790t91TtnY4fKSZMmeXzllVcC5aUmf/M3f+OxdYL79a9/XfGxLP9ieXXMSeviBvDJT34SaC2VARg0aJDH9hgvvPCCj8WORirhzl+987Y922+/vceXXdb69FYSa+WE0Ln8iV0PLY6/I3HfnDx1ak1ISmkwMAx4EuhXejMB1gD92viZscDYSl8TqTXlrBSNclaKRjkrRaS8rb8OX4SklHYGpgDjsizbGBdnZVmWpZQq3grLsmw8ML70GFVb5fLMM88A8Mgjj/jYiBEjPLaF53GRWFzEaDuU2uNA634fAM8995zHmvUopkbL2VqIu6cOHTrUY1tcGXeU1mL0xlevnI17HD366KMeW+/4E044wcduueUWj+2cGRe2x513bbFjbPoR777F12d35+ICygsvvNBj2+laGku9cjaez26++WaPTzvtNKB1lgLKGyucd955AAwZMsTHYs7FReom5mxsWjN69GigvClO3Cdkzpw5AEybNs3HbFHwlq9B8tWonw9irsXPtDY+fPhwH4sNDyrtUxcrJY466qitHivOjsyfP9/jPPOyQy16U0q9aHmzJmZZZjutrU0p9S99vT/wem0OUaTzlLNSNMpZKRrlrBSR8rZxdKQ7VgJuBRZmWXZD+NI0YEwpHgNMrf7hiXSeclaKRjkrRaOclSJS3jaW1N60S0ppBPAY8Bxgq2CuoKWGbjKwN7AcODPLsg9t2l6LqatYjvLxj3/cY1tQGRc2Llq0yGNbiPnBBx/4WINNjT6VZdmh9T6IImr0nK2mWBoQSxOtHCuWxixbtqzWh6Oc7aJGylnbGwRg+vTpQGtZFpQv0rVzZjx3xhIAK3GIX7fcBHjllVc8vvjiiwF46KGHfKy9PvhVoJztokbK2VhKs//++wPwox/9yMcOOuggj20BbmzGEEtZKuVZXLj+zjvvbPW8cb+PefPmeXz//fcD5c0UYk53cWG6crYbGilvK+nfv7/HsTTW9r5bsWKFj/30pz/1+KmnnrJj8rG490dsXLP77rsD5eVco0aN8jiWaVVLlmWp0nhHumPNACr+MHBcG+MidaOclaJRzkrRKGeliJS3jaVDa0JERERERESqpd1yrKo+WYOXtjQYTbk2gEbM2Tjd+vWvf93j//iP//B48+bNABxyyCE+Zv3xa0g52wC6m7Ox3Opzn/scANddd52Pxa5Csae9iSUmGzZsAFq7BAH853/+p8exhNBKYnIui1XONoBal2rHstXPf/7zQGvJNpTnvJUT/ulPf/Kx2DFrzZo1HlteL1iwwMfefvttj+13IZYgViG/lbMNohZ5G/fruOqqqzy+6KKLgPJ9arY4FqA8l6N4Xl61ahUAZ5xxho/FDnG10FY5lmZCREREREQkV53arFBEGku8axL7069duxbQLunSefGO2b333gvAgw8+6GMjR4702HaKjneKbTEutO4UHRfzNlgDEGlScbH57NmzPbZZi759+/pY7969Pd5hhx2A8jx98803PX799dbOrbZgPc6axJ+r1JhB5MPEWbNrr73W41mzZgEwbtw4Hzv00NYJsZ122gkob7YUF5jbuRzgyiuvBGD58uXVOuwu00yIiIiIiIjkShchIiIiIiKSKy1Mb1xafNYAGj1n4yI06yMO8NZbbwHlZTA5UM42gEbP2QajnG0AeeasnTNjg4/IyhEbuIRKOdsgdK7tOC1MFxERERGRhqCLEBERERERyZW6Y4kUWOxklMM+ICIihRbPmSJSX5oJERERERGRXOkiREREREREcqWLEBERERERyZUuQkREREREJFd5L0xfB2wu/dts9qC6r2ufKj6WdJ1ytuOUs41hHbCc6r+/jUA525yUsx2nnG0czfr5ILeczXWzQoCU0txm3GinWV+XNO9726yvS1o04/vbjK9JWjXj+9uMr0laNeP7m+drUjmWiIiIiIjkShchIiIiIiKSq3pchIyvw3PmoVlflzTve9usr0taNOP724yvSVo14/vbjK9JWjXj+5vba8p9TYiIiIiIiPRsKscSEREREZFc6SJERERERERyletFSErppJTSiymll1JKl+f53NWSUhqUUnoopbQgpfRCSumi0vhuKaUHUkpLSv/2qfexSvcpZ6VomiFnQXnbkyhnpWiUs1V6/rzWhKSUtgEWAycAK4E5wFlZli3I5QCqJKXUH+ifZdm8lFJv4CngNODvgfVZlv2wlJB9siy7rI6HKt2knJWiaZacBeVtT6GclaJRzlZPnjMhw4GXsixbmmXZH4BJwKk5Pn9VZFm2OsuyeaV4E7AQGEjLa5lQ+rYJtLyJUmzKWSmapshZUN72IMpZKRrlbJXkeREyEFgR/r+yNFZYKaXBwDDgSaBflmWrS19aA/Sr02FJ9ShnpWiaLmdBedvklLNSNMrZKtHC9C5KKe0MTAHGZVm2MX4ta6lxU+9jaSjKWSki5a0UjXJWiqZeOZvnRchrwKDw/71KY4WTUupFy5s1Mcuyu0vDa0u1dVZj93q9jk+qRjkrRdM0OQvK2x5COStFo5ytkjwvQuYAB6aU9k0pbQf8LTAtx+evipRSAm4FFmZZdkP40jRgTCkeA0zN+9ik6pSzUjRNkbOgvO1BlLNSNMrZaj1/njump5ROBv4fsA1wW5Zl/5Lbk1dJSmkE8BjwHPDn0vAVtNTQTQb2BpYDZ2ZZtr4uBylVo5yVommGnAXlbU+inJWiUc5W6fnzvAgRERERERHRwnQREREREcmVLkJERERERCRXuggREREREZFc6SJERERERERypYsQERERERHJlS5CREREREQkV7oIERERERGRXP1/12oknek+Fn8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "samples = torch.randn(5, 5, model.representation_length)\n",
+    "\n",
+    "fig, axes = plt.subplots(5, 5)\n",
+    "\n",
+    "for i in range(5):\n",
+    "    for j in range(5):\n",
+    "        decoded = model.decode(samples[i, j][None].to(config.device))[0].detach()\n",
+    "        show_image(axes[i, j], decoded.cpu())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:01<00:00,  7.01it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bs = 1_024\n",
+    "\n",
+    "results = []\n",
+    "\n",
+    "for i in tqdm.trange(validation_dataset.num_batches(bs)):\n",
+    "    nll = model.nll(validation_dataset.get_batch(i, bs)['x'].to(config.device))\n",
+    "\n",
+    "    results.append(nll.cpu().numpy())\n",
+    "\n",
+    "\n",
+    "full_results = np.concatenate(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90.79018"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(full_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:01<00:00,  6.69it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bs = 1_024\n",
+    "\n",
+    "results = []\n",
+    "\n",
+    "for i in tqdm.trange(validation_dataset.num_batches(bs)):\n",
+    "    nll = model.nll(validation_dataset.get_batch(i, bs)['x'].to(config.device), num_posterior_samples=5)\n",
+    "\n",
+    "    results.append(nll.cpu().numpy())\n",
+    "\n",
+    "\n",
+    "full_results = np.concatenate(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "88.15819"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(full_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:01<00:00,  5.54it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bs = 1_024\n",
+    "\n",
+    "results = []\n",
+    "\n",
+    "for i in tqdm.trange(validation_dataset.num_batches(bs)):\n",
+    "    nll = model.nll(validation_dataset.get_batch(i, bs)['x'].to(config.device), num_posterior_samples=50)\n",
+    "\n",
+    "    results.append(nll.cpu().numpy())\n",
+    "\n",
+    "\n",
+    "full_results = np.concatenate(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "87.12366"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(full_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:04<00:00,  2.24it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bs = 1_024\n",
+    "\n",
+    "results = []\n",
+    "\n",
+    "for i in tqdm.trange(validation_dataset.num_batches(bs)):\n",
+    "    nll = model.nll(validation_dataset.get_batch(i, bs)['x'].to(config.device), num_posterior_samples=500)\n",
+    "\n",
+    "    results.append(nll.cpu().numpy())\n",
+    "\n",
+    "\n",
+    "full_results = np.concatenate(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "86.84696"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(full_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:12<00:00,  1.28s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bs = 1_024\n",
+    "\n",
+    "results = []\n",
+    "\n",
+    "for i in tqdm.trange(validation_dataset.num_batches(bs)):\n",
+    "    nll = model.nll(validation_dataset.get_batch(i, bs)['x'].to(config.device), num_posterior_samples=2000)\n",
+    "\n",
+    "    results.append(nll.cpu().numpy())\n",
+    "\n",
+    "\n",
+    "full_results = np.concatenate(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "86.82053"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(full_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idx1 = 1\n",
+    "idx2 = 5\n",
+    "\n",
+    "N = 10\n",
+    "\n",
+    "encoding1 = model.encode(get_sample(idx1)[None])\n",
+    "encoding2 = model.encode(get_sample(idx2)[None])\n",
+    "\n",
+    "fig, axes = plt.subplots(1, N)\n",
+    "\n",
+    "for i in range(10):\n",
+    "    beta = float(i) / float(N - 1)\n",
+    "    alpha = 1.0 - beta\n",
+    "    \n",
+    "    combined =  model.decoder(encoding1 * alpha + encoding2 * beta)[0]\n",
+    "    \n",
+    "    show_image(axes[i], combined.cpu())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "vel",
+   "language": "python",
+   "name": "vel"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples-notebooks/autoencoders/mnist/mnist-vq-vae.ipynb b/examples-notebooks/autoencoders/mnist/mnist-vq-vae.ipynb
new file mode 100644
index 00000000..32f55f3c
--- /dev/null
+++ b/examples-notebooks/autoencoders/mnist/mnist-vq-vae.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import tqdm\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import vel\n",
+    "import vel.notebook as nb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nb.reasonable_notbook_defaults()\n",
+    "torch.set_grad_enabled(False)  # We don't need autograd here\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = nb.load_config('examples-configs/latent/mnist/mnist_cnn_vq_vae.yaml', run_number=13, device='cuda:0') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = config.load_trained_model().to(config.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VQVAE(\n",
+      "  (codebook): VQEmbedding(k=128, d=64)\n",
+      "  (encoder): Sequential(\n",
+      "    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (1): SELU(inplace=True)\n",
+      "    (2): LayerNorm((64, 28, 28), eps=1e-05, elementwise_affine=True)\n",
+      "    (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+      "    (4): SELU(inplace=True)\n",
+      "    (5): LayerNorm((128, 14, 14), eps=1e-05, elementwise_affine=True)\n",
+      "    (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+      "    (7): SELU(inplace=True)\n",
+      "    (8): LayerNorm((256, 7, 7), eps=1e-05, elementwise_affine=True)\n",
+      "    (9): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (10): SELU(inplace=True)\n",
+      "    (11): LayerNorm((64, 7, 7), eps=1e-05, elementwise_affine=True)\n",
+      "  )\n",
+      "  (decoder): Sequential(\n",
+      "    (0): ConvTranspose2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (1): SELU(inplace=True)\n",
+      "    (2): LayerNorm((256, 7, 7), eps=1e-05, elementwise_affine=True)\n",
+      "    (3): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
+      "    (4): SELU(inplace=True)\n",
+      "    (5): LayerNorm((128, 14, 14), eps=1e-05, elementwise_affine=True)\n",
+      "    (6): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
+      "    (7): SELU(inplace=True)\n",
+      "    (8): LayerNorm((64, 28, 28), eps=1e-05, elementwise_affine=True)\n",
+      "    (9): ConvTranspose2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (10): Sigmoid()\n",
+      "  )\n",
+      ")\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Number of model parameters: 1,400,001\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_loader = config.provide('loader')\n",
+    "data_source = data_loader.transformed_source\n",
+    "train_dataset = data_source.train\n",
+    "validation_dataset = data_source.validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_sample(idx):\n",
+    "    return train_dataset[idx]['x'].to(config.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_image(axis, sample):\n",
+    "    axis.imshow(train_dataset.denormalize_item(sample, 'x'), cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Browse examples\n",
+    "fig, axes = plt.subplots(1, 5)\n",
+    "\n",
+    "for index in range(5):\n",
+    "    show_image(axes[index], get_sample(index).cpu())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[ 23,  23,  23,  23,  23,  23,  23],\n",
+      "         [ 23,  64,  88, 106,   4, 121,  29],\n",
+      "         [ 23,  70, 114,  51,  31,  12,  12],\n",
+      "         [ 23,  23,  46, 117,  22,  23,  23],\n",
+      "         [ 23,  23,  23,  70, 114, 104,  23],\n",
+      "         [ 23,  22,   4,  65, 116,  29,  23],\n",
+      "         [ 99, 116,  51, 110,  23,  23,  23]]], device='cuda:0')\n",
+      "torch.Size([1, 7, 7])\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = model.encode(get_sample(0)[None])\n",
+    "print(x)\n",
+    "print(x.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Browse examples\n",
+    "fig, axes = plt.subplots(2, 5)\n",
+    "\n",
+    "for index in range(5):\n",
+    "    sample = get_sample(index)\n",
+    "    decoded = model(sample[None])[0].detach()\n",
+    "    show_image(axes[0, index], sample.cpu())\n",
+    "    show_image(axes[1, index], decoded.cpu())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "vel",
+   "language": "python",
+   "name": "vel"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples-scripts/rl/atari/a2c/breakout_a2c.py b/examples-scripts/rl/atari/a2c/breakout_a2c.py
deleted file mode 100644
index 1f855788..00000000
--- a/examples-scripts/rl/atari/a2c/breakout_a2c.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.optim as optim
-
-from vel.rl.metrics import EpisodeRewardMetric
-from vel.storage.streaming.stdout import StdoutStreaming
-from vel.util.random import set_seed
-
-from vel.rl.env.classic_atari import ClassicAtariEnv
-from vel.rl.vecenv.subproc import SubprocVecEnvWrapper
-
-from vel.modules.input.image_to_tensor import ImageToTensorFactory
-from vel.rl.models.stochastic_policy_model import StochasticPolicyModelFactory
-from vel.rl.models.backbone.nature_cnn import NatureCnnFactory
-
-
-from vel.rl.reinforcers.on_policy_iteration_reinforcer import (
-    OnPolicyIterationReinforcer, OnPolicyIterationReinforcerSettings
-)
-
-from vel.rl.algo.policy_gradient.a2c import A2CPolicyGradient
-from vel.rl.env_roller.step_env_roller import StepEnvRoller
-
-from vel.api.info import TrainingInfo, EpochInfo
-
-
-def breakout_a2c():
-    device = torch.device('cuda:0')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Create 16 environments evaluated in parallel in sub processess with all usual DeepMind wrappers
-    # These are just helper functions for that
-    vec_env = SubprocVecEnvWrapper(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=16, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model = StochasticPolicyModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    ).instantiate(action_space=vec_env.action_space)
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = OnPolicyIterationReinforcer(
-        device=device,
-        settings=OnPolicyIterationReinforcerSettings(
-            batch_size=256,
-            number_of_steps=5,
-        ),
-        model=model,
-        algo=A2CPolicyGradient(
-            entropy_coefficient=0.01,
-            value_coefficient=0.5,
-            max_grad_norm=0.5,
-            discount_factor=0.99,
-        ),
-        env_roller=StepEnvRoller(
-            environment=vec_env,
-            device=device,
-        )
-    )
-
-    # Model optimizer
-    optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=7.0e-4, eps=1e-3)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[StdoutStreaming()]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = int(1.1e7 / (5 * 16) / 100)
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=100,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info)
-
-    training_info.on_train_end()
-
-
-if __name__ == '__main__':
-    breakout_a2c()
diff --git a/examples-scripts/rl/atari/a2c/breakout_a2c_evaluate.py b/examples-scripts/rl/atari/a2c/breakout_a2c_evaluate.py
deleted file mode 100644
index 2912d46e..00000000
--- a/examples-scripts/rl/atari/a2c/breakout_a2c_evaluate.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import torch
-import pandas as pd
-import numpy as np
-
-from vel.modules.input.image_to_tensor import ImageToTensorFactory
-from vel.openai.baselines.common.atari_wrappers import FrameStack
-from vel.rl.env.classic_atari import ClassicAtariEnv
-from vel.rl.models.backbone.nature_cnn import NatureCnnFactory
-from vel.rl.models.stochastic_policy_model import StochasticPolicyModelFactory
-
-
-def breakout_a2c_evaluate(checkpoint_file_path, takes=10):
-    model_checkpoint = torch.load(checkpoint_file_path)
-    device = torch.device('cuda:0')
-
-    env = FrameStack(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4').instantiate(preset='record'), k=4
-    )
-
-    model = StochasticPolicyModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    ).instantiate(action_space=env.action_space)
-
-    model.load_state_dict(model_checkpoint)
-    model = model.to(device)
-
-    model.eval()
-
-    rewards = []
-    lengths = []
-
-    for i in range(takes):
-        result = record_take(model, env, device)
-        rewards.append(result['r'])
-        lengths.append(result['l'])
-
-    print(pd.DataFrame({'lengths': lengths, 'rewards': rewards}).describe())
-
-
-@torch.no_grad()
-def record_take(model, env_instance, device):
-    frames = []
-
-    observation = env_instance.reset()
-
-    frames.append(env_instance.render('rgb_array'))
-
-    print("Evaluating environment...")
-
-    while True:
-        observation_array = np.expand_dims(np.array(observation), axis=0)
-        observation_tensor = torch.from_numpy(observation_array).to(device)
-        actions = model.step(observation_tensor, argmax_sampling=True)['actions']
-
-        observation, reward, done, epinfo = env_instance.step(actions.item())
-
-        frames.append(env_instance.render('rgb_array'))
-
-        if 'episode' in epinfo:
-            # End of an episode
-            return epinfo['episode']
-
-
-if __name__ == '__main__':
-    breakout_a2c_evaluate("checkpoint_00001375.data", takes=2)
diff --git a/examples-scripts/rl/atari/ppo/qbert_ppo.py b/examples-scripts/rl/atari/ppo/qbert_ppo.py
deleted file mode 100644
index 98388dd1..00000000
--- a/examples-scripts/rl/atari/ppo/qbert_ppo.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import torch
-import torch.optim as optim
-
-from vel.rl.metrics import EpisodeRewardMetric
-from vel.storage.streaming.stdout import StdoutStreaming
-from vel.util.random import set_seed
-from vel.api.info import TrainingInfo, EpochInfo
-
-from vel.modules.input.image_to_tensor import ImageToTensorFactory
-from vel.rl.env.classic_atari import ClassicAtariEnv
-from vel.rl.vecenv.subproc import SubprocVecEnvWrapper
-from vel.rl.models.stochastic_policy_model import StochasticPolicyModelFactory
-from vel.rl.models.backbone.nature_cnn import NatureCnnFactory
-
-from vel.rl.reinforcers.on_policy_iteration_reinforcer import (
-    OnPolicyIterationReinforcer, OnPolicyIterationReinforcerSettings
-)
-
-from vel.rl.algo.policy_gradient.ppo import PpoPolicyGradient
-from vel.rl.env_roller.step_env_roller import StepEnvRoller
-from vel.rl.commands.rl_train_command import FrameTracker
-
-from vel.schedules.linear import LinearSchedule
-
-
-def qbert_ppo():
-    device = torch.device('cuda:0')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Create 16 environments evaluated in parallel in sub processess with all usual DeepMind wrappers
-    # These are just helper functions for that
-    vec_env = SubprocVecEnvWrapper(
-        ClassicAtariEnv('QbertNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=8, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model = StochasticPolicyModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    ).instantiate(action_space=vec_env.action_space)
-
-    # Set schedule for gradient clipping.
-    cliprange = LinearSchedule(
-        initial_value=0.1,
-        final_value=0.0
-    )
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = OnPolicyIterationReinforcer(
-        device=device,
-        settings=OnPolicyIterationReinforcerSettings(
-            batch_size=256,
-            experience_replay=4,
-            number_of_steps=128
-        ),
-        model=model,
-        algo=PpoPolicyGradient(
-            entropy_coefficient=0.01,
-            value_coefficient=0.5,
-            max_grad_norm=0.5,
-            discount_factor=0.99,
-            gae_lambda=0.95,
-            cliprange=cliprange
-        ),
-        env_roller=StepEnvRoller(
-            environment=vec_env,
-            device=device,
-        )
-    )
-
-    # Model optimizer
-    optimizer = optim.Adam(reinforcer.model.parameters(), lr=2.5e-4, eps=1.0e-5)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[
-            StdoutStreaming(),   # Print live metrics every epoch to standard output
-            FrameTracker(1.1e7)      # We need frame tracker to track the progress of learning
-        ]
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 10 batches per epoch to average metrics nicely
-    # Rollout size is 8 environments times 128 steps
-    num_epochs = int(1.1e7 / (128 * 8) / 10)
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=10,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info)
-
-    training_info.on_train_end()
-
-
-if __name__ == '__main__':
-    qbert_ppo()
diff --git a/examples-scripts/rl/mujoco/ddpg/half_cheetah_ddpg.py b/examples-scripts/rl/mujoco/ddpg/half_cheetah_ddpg.py
deleted file mode 100644
index 6b0e200b..00000000
--- a/examples-scripts/rl/mujoco/ddpg/half_cheetah_ddpg.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import torch
-import torch.optim
-
-from vel.api import TrainingInfo, EpochInfo
-from vel.modules.input.normalize_observations import NormalizeObservationsFactory
-from vel.rl.buffers.circular_replay_buffer import CircularReplayBuffer
-from vel.rl.env_roller.transition_replay_env_roller import TransitionReplayEnvRoller
-from vel.rl.metrics import EpisodeRewardMetric
-from vel.rl.modules.noise.ou_noise import OuNoise
-from vel.storage.streaming.stdout import StdoutStreaming
-from vel.util.random import set_seed
-from vel.rl.env.mujoco import MujocoEnv
-from vel.rl.models.deterministic_policy_model import DeterministicPolicyModelFactory
-from vel.rl.models.backbone.mlp import MLPFactory
-from vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer import (
-    BufferedOffPolicyIterationReinforcer, BufferedOffPolicyIterationReinforcerSettings
-)
-from vel.rl.algo.policy_gradient.ddpg import DeepDeterministicPolicyGradient
-from vel.rl.vecenv.dummy import DummyVecEnvWrapper
-from vel.optimizers.adam import AdamFactory
-
-
-def half_cheetah_ddpg():
-    device = torch.device('cuda:0')
-    seed = 1002
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    vec_env = DummyVecEnvWrapper(
-        MujocoEnv('HalfCheetah-v2')
-    ).instantiate(parallel_envs=1, seed=seed)
-
-    model_factory = DeterministicPolicyModelFactory(
-        input_block=NormalizeObservationsFactory(input_shape=17),
-        policy_backbone=MLPFactory(input_length=17, hidden_layers=[64, 64], activation='tanh'),
-        value_backbone=MLPFactory(input_length=23, hidden_layers=[64, 64], activation='tanh'),
-    )
-
-    model = model_factory.instantiate(action_space=vec_env.action_space)
-
-    reinforcer = BufferedOffPolicyIterationReinforcer(
-        device=device,
-        environment=vec_env,
-        settings=BufferedOffPolicyIterationReinforcerSettings(
-            rollout_steps=2,
-            training_steps=64,
-        ),
-        model=model,
-        algo=DeepDeterministicPolicyGradient(
-            model_factory=model_factory,
-            discount_factor=0.99,
-            tau=0.01,
-        ),
-        env_roller=TransitionReplayEnvRoller(
-            environment=vec_env,
-            device=device,
-            action_noise=OuNoise(std_dev=0.2, environment=vec_env),
-            replay_buffer=CircularReplayBuffer(
-                buffer_capacity=1_000_000,
-                buffer_initial_size=2_000,
-                num_envs=vec_env.num_envs,
-                observation_space=vec_env.observation_space,
-                action_space=vec_env.action_space
-            ),
-            normalize_returns=True,
-            discount_factor=0.99
-        ),
-    )
-
-    # Optimizer helper - A weird regularization settings I've copied from OpenAI code
-    adam_optimizer = AdamFactory(
-        lr=[1.0e-4, 1.0e-3, 1.0e-3],
-        weight_decay=[0.0, 0.0, 0.001],
-        eps=1.0e-4,
-        layer_groups=True
-    ).instantiate(model)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[StdoutStreaming()]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 20 batches per epoch to average metrics nicely
-    num_epochs = int(1.0e6 / 2 / 1000)
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1000,
-            optimizer=adam_optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info)
-
-    training_info.on_train_end()
-
-
-if __name__ == '__main__':
-    half_cheetah_ddpg()
diff --git a/examples-scripts/run_config.py b/examples-scripts/run_config.py
new file mode 100644
index 00000000..18200680
--- /dev/null
+++ b/examples-scripts/run_config.py
@@ -0,0 +1,17 @@
+import os.path
+
+
+from vel.api import ModelConfig
+
+project_dir = ModelConfig.find_project_directory(os.getcwd())
+
+model_config = ModelConfig.from_file(
+    filename=os.path.join(
+        project_dir, 'examples-configs/generative-likelihood/mnist/mnist_cnn_iwae.yaml',
+    ),
+    run_number=2,
+)
+
+model_config.set_seed()
+model_config.banner('train')
+model_config.run_command('train')
diff --git a/notebooks/classic/mnist_cnn_01.ipynb b/notebooks/classic/mnist_cnn_01.ipynb
new file mode 100644
index 00000000..164dd088
--- /dev/null
+++ b/notebooks/classic/mnist_cnn_01.ipynb
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding /mnt/fast-data/repos/waterboy to the PYTHONPATH\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize pythonpath to include waterboy. I don't know the way how to walk around this in other way\n",
+    "import os.path\n",
+    "import sys\n",
+    "\n",
+    "path = os.path.abspath('.')\n",
+    "\n",
+    "while True:\n",
+    "        \n",
+    "    if os.path.basename(path) == 'waterboy':\n",
+    "        if path not in sys.path:\n",
+    "            print(f'Adding {path} to the PYTHONPATH')\n",
+    "            sys.path.append(path)\n",
+    "        break\n",
+    "    else:\n",
+    "        up_path = os.path.realpath(os.path.join(path, '..'))\n",
+    "        if path == up_path:\n",
+    "            break\n",
+    "        else:\n",
+    "            path = up_path                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import waterboy.notebook as nb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'waterboy.notebook' from '/mnt/fast-data/repos/waterboy/waterboy/notebook/__init__.py'>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import importlib\n",
+    "importlib.reload(nb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model_config = nb.load(\"../../examples/classification/mnist/mnist_cnn_01.yaml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<ModelConfig at ../../examples/classification/mnist/mnist_cnn_01.yaml"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'waterboy.internals.model_config' from '/mnt/fast-data/repos/waterboy/waterboy/internals/model_config.py'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import waterboy.internals.model_config\n",
+    "importlib.reload(waterboy.internals.model_config)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "waterboy",
+   "language": "python",
+   "name": "waterboy"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/imagenet_transfer/cats_vs_dogs.ipynb b/notebooks/imagenet_transfer/cats_vs_dogs.ipynb
new file mode 100644
index 00000000..a8d657d3
--- /dev/null
+++ b/notebooks/imagenet_transfer/cats_vs_dogs.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adding /mnt/fast-data/repos/waterboy to the PYTHONPATH\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize pythonpath to include waterboy. I don't know the way how to walk around this in other way\n",
+    "import os.path\n",
+    "import sys\n",
+    "\n",
+    "path = os.path.abspath('.')\n",
+    "\n",
+    "while True:\n",
+    "        \n",
+    "    if os.path.basename(path) == 'waterboy':\n",
+    "        if path not in sys.path:\n",
+    "            print(f'Adding {path} to the PYTHONPATH')\n",
+    "            sys.path.append(path)\n",
+    "        break\n",
+    "    else:\n",
+    "        up_path = os.path.realpath(os.path.join(path, '..'))\n",
+    "        if path == up_path:\n",
+    "            break\n",
+    "        else:\n",
+    "            path = up_path "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import waterboy.notebook as nb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model_config = nb.load(\"../../examples/classification/imagenet_transfer/cats_vs_dogs_resnet34.yaml\", device='cuda:1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================================\n",
+      "Pytorch version: 0.4.0 cuda version 9.1.85 cudnn version 7102\n",
+      "Running model cats_vs_dogs_resnet34, run 0 -- command notebook -- device cuda:1\n",
+      "CUDA Device name GeForce GTX 1080 Ti\n",
+      "2018/06/10 - 12:19:38\n",
+      "================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_config.banner(\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "model = model_config.provide(\"model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model.model.load_state_dict(torch.load(\"/tmp/weight.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1         [-1, 64, 112, 112]           9,408\n",
+      "       BatchNorm2d-2         [-1, 64, 112, 112]             128\n",
+      "              ReLU-3         [-1, 64, 112, 112]               0\n",
+      "         MaxPool2d-4           [-1, 64, 56, 56]               0\n",
+      "            Conv2d-5           [-1, 64, 56, 56]          36,864\n",
+      "       BatchNorm2d-6           [-1, 64, 56, 56]             128\n",
+      "              ReLU-7           [-1, 64, 56, 56]               0\n",
+      "            Conv2d-8           [-1, 64, 56, 56]          36,864\n",
+      "       BatchNorm2d-9           [-1, 64, 56, 56]             128\n",
+      "             ReLU-10           [-1, 64, 56, 56]               0\n",
+      "       BasicBlock-11           [-1, 64, 56, 56]               0\n",
+      "           Conv2d-12           [-1, 64, 56, 56]          36,864\n",
+      "      BatchNorm2d-13           [-1, 64, 56, 56]             128\n",
+      "             ReLU-14           [-1, 64, 56, 56]               0\n",
+      "           Conv2d-15           [-1, 64, 56, 56]          36,864\n",
+      "      BatchNorm2d-16           [-1, 64, 56, 56]             128\n",
+      "             ReLU-17           [-1, 64, 56, 56]               0\n",
+      "       BasicBlock-18           [-1, 64, 56, 56]               0\n",
+      "           Conv2d-19           [-1, 64, 56, 56]          36,864\n",
+      "      BatchNorm2d-20           [-1, 64, 56, 56]             128\n",
+      "             ReLU-21           [-1, 64, 56, 56]               0\n",
+      "           Conv2d-22           [-1, 64, 56, 56]          36,864\n",
+      "      BatchNorm2d-23           [-1, 64, 56, 56]             128\n",
+      "             ReLU-24           [-1, 64, 56, 56]               0\n",
+      "       BasicBlock-25           [-1, 64, 56, 56]               0\n",
+      "           Conv2d-26          [-1, 128, 28, 28]          73,728\n",
+      "      BatchNorm2d-27          [-1, 128, 28, 28]             256\n",
+      "             ReLU-28          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-29          [-1, 128, 28, 28]         147,456\n",
+      "      BatchNorm2d-30          [-1, 128, 28, 28]             256\n",
+      "           Conv2d-31          [-1, 128, 28, 28]           8,192\n",
+      "      BatchNorm2d-32          [-1, 128, 28, 28]             256\n",
+      "             ReLU-33          [-1, 128, 28, 28]               0\n",
+      "       BasicBlock-34          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-35          [-1, 128, 28, 28]         147,456\n",
+      "      BatchNorm2d-36          [-1, 128, 28, 28]             256\n",
+      "             ReLU-37          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-38          [-1, 128, 28, 28]         147,456\n",
+      "      BatchNorm2d-39          [-1, 128, 28, 28]             256\n",
+      "             ReLU-40          [-1, 128, 28, 28]               0\n",
+      "       BasicBlock-41          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-42          [-1, 128, 28, 28]         147,456\n",
+      "      BatchNorm2d-43          [-1, 128, 28, 28]             256\n",
+      "             ReLU-44          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-45          [-1, 128, 28, 28]         147,456\n",
+      "      BatchNorm2d-46          [-1, 128, 28, 28]             256\n",
+      "             ReLU-47          [-1, 128, 28, 28]               0\n",
+      "       BasicBlock-48          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-49          [-1, 128, 28, 28]         147,456\n",
+      "      BatchNorm2d-50          [-1, 128, 28, 28]             256\n",
+      "             ReLU-51          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-52          [-1, 128, 28, 28]         147,456\n",
+      "      BatchNorm2d-53          [-1, 128, 28, 28]             256\n",
+      "             ReLU-54          [-1, 128, 28, 28]               0\n",
+      "       BasicBlock-55          [-1, 128, 28, 28]               0\n",
+      "           Conv2d-56          [-1, 256, 14, 14]         294,912\n",
+      "      BatchNorm2d-57          [-1, 256, 14, 14]             512\n",
+      "             ReLU-58          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-59          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-60          [-1, 256, 14, 14]             512\n",
+      "           Conv2d-61          [-1, 256, 14, 14]          32,768\n",
+      "      BatchNorm2d-62          [-1, 256, 14, 14]             512\n",
+      "             ReLU-63          [-1, 256, 14, 14]               0\n",
+      "       BasicBlock-64          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-65          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-66          [-1, 256, 14, 14]             512\n",
+      "             ReLU-67          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-68          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-69          [-1, 256, 14, 14]             512\n",
+      "             ReLU-70          [-1, 256, 14, 14]               0\n",
+      "       BasicBlock-71          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-72          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-73          [-1, 256, 14, 14]             512\n",
+      "             ReLU-74          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-75          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-76          [-1, 256, 14, 14]             512\n",
+      "             ReLU-77          [-1, 256, 14, 14]               0\n",
+      "       BasicBlock-78          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-79          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-80          [-1, 256, 14, 14]             512\n",
+      "             ReLU-81          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-82          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-83          [-1, 256, 14, 14]             512\n",
+      "             ReLU-84          [-1, 256, 14, 14]               0\n",
+      "       BasicBlock-85          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-86          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-87          [-1, 256, 14, 14]             512\n",
+      "             ReLU-88          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-89          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-90          [-1, 256, 14, 14]             512\n",
+      "             ReLU-91          [-1, 256, 14, 14]               0\n",
+      "       BasicBlock-92          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-93          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-94          [-1, 256, 14, 14]             512\n",
+      "             ReLU-95          [-1, 256, 14, 14]               0\n",
+      "           Conv2d-96          [-1, 256, 14, 14]         589,824\n",
+      "      BatchNorm2d-97          [-1, 256, 14, 14]             512\n",
+      "             ReLU-98          [-1, 256, 14, 14]               0\n",
+      "       BasicBlock-99          [-1, 256, 14, 14]               0\n",
+      "          Conv2d-100            [-1, 512, 7, 7]       1,179,648\n",
+      "     BatchNorm2d-101            [-1, 512, 7, 7]           1,024\n",
+      "            ReLU-102            [-1, 512, 7, 7]               0\n",
+      "          Conv2d-103            [-1, 512, 7, 7]       2,359,296\n",
+      "     BatchNorm2d-104            [-1, 512, 7, 7]           1,024\n",
+      "          Conv2d-105            [-1, 512, 7, 7]         131,072\n",
+      "     BatchNorm2d-106            [-1, 512, 7, 7]           1,024\n",
+      "            ReLU-107            [-1, 512, 7, 7]               0\n",
+      "      BasicBlock-108            [-1, 512, 7, 7]               0\n",
+      "          Conv2d-109            [-1, 512, 7, 7]       2,359,296\n",
+      "     BatchNorm2d-110            [-1, 512, 7, 7]           1,024\n",
+      "            ReLU-111            [-1, 512, 7, 7]               0\n",
+      "          Conv2d-112            [-1, 512, 7, 7]       2,359,296\n",
+      "     BatchNorm2d-113            [-1, 512, 7, 7]           1,024\n",
+      "            ReLU-114            [-1, 512, 7, 7]               0\n",
+      "      BasicBlock-115            [-1, 512, 7, 7]               0\n",
+      "          Conv2d-116            [-1, 512, 7, 7]       2,359,296\n",
+      "     BatchNorm2d-117            [-1, 512, 7, 7]           1,024\n",
+      "            ReLU-118            [-1, 512, 7, 7]               0\n",
+      "          Conv2d-119            [-1, 512, 7, 7]       2,359,296\n",
+      "     BatchNorm2d-120            [-1, 512, 7, 7]           1,024\n",
+      "            ReLU-121            [-1, 512, 7, 7]               0\n",
+      "      BasicBlock-122            [-1, 512, 7, 7]               0\n",
+      "AdaptiveMaxPool2d-123            [-1, 512, 1, 1]               0\n",
+      "AdaptiveAvgPool2d-124            [-1, 512, 1, 1]               0\n",
+      "AdaptiveConcatPool2d-125           [-1, 1024, 1, 1]               0\n",
+      "         Flatten-126                 [-1, 1024]               0\n",
+      "     BatchNorm1d-127                 [-1, 1024]           2,048\n",
+      "         Dropout-128                 [-1, 1024]               0\n",
+      "          Linear-129                  [-1, 512]         524,800\n",
+      "            ReLU-130                  [-1, 512]               0\n",
+      "     BatchNorm1d-131                  [-1, 512]           1,024\n",
+      "         Dropout-132                  [-1, 512]               0\n",
+      "          Linear-133                    [-1, 2]           1,026\n",
+      "      LogSoftmax-134                    [-1, 2]               0\n",
+      "================================================================\n",
+      "Total params: 21,813,570\n",
+      "Trainable params: 21,813,570\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary(input_size=(3, 224, 224))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "run_command = model_config.get_command('train')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Epoch: 0001 [000000/023000 (00%)]\tloss: 0.854900 accuracy: 0.562500\n",
+      "Train Epoch: 0001 [006400/023000 (28%)]\tloss: 0.150858 accuracy: 0.939202\n",
+      "Train Epoch: 0001 [012800/023000 (56%)]\tloss: 0.117378 accuracy: 0.953358\n",
+      "Train Epoch: 0001 [019200/023000 (83%)]\tloss: 0.104418 accuracy: 0.958576\n",
+      "=>>>>>>>>>> EPOCH 1\n",
+      "Train      accuracy 0.960822 loss 0.100100\n",
+      "Validation accuracy 0.983887 loss 0.035918\n",
+      "=>>>>>>>>>> DONE\n",
+      "Train Epoch: 0002 [000000/023000 (00%)]\tloss: 0.058765 accuracy: 0.953125\n",
+      "Train Epoch: 0002 [006400/023000 (28%)]\tloss: 0.076278 accuracy: 0.971225\n",
+      "Train Epoch: 0002 [012800/023000 (56%)]\tloss: 0.071321 accuracy: 0.973025\n",
+      "Train Epoch: 0002 [019200/023000 (83%)]\tloss: 0.069153 accuracy: 0.973681\n",
+      "=>>>>>>>>>> EPOCH 2\n",
+      "Train      accuracy 0.973799 loss 0.069813\n",
+      "Validation accuracy 0.983887 loss 0.037188\n",
+      "=>>>>>>>>>> DONE\n"
+     ]
+    }
+   ],
+   "source": [
+    "run_command.run()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "waterboy",
+   "language": "python",
+   "name": "waterboy"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.in b/requirements.in
new file mode 100644
index 00000000..979d9ef7
--- /dev/null
+++ b/requirements.in
@@ -0,0 +1,17 @@
+attrs
+cloudpickle
+dnspython
+gym[atari,box2d,classic_control]
+matplotlib
+numpy
+opencv-python
+pandas
+pymongo
+pytest
+pyyaml
+scikit-learn
+torchtext
+torchvision
+torch~=1.3.1
+tqdm
+visdom
diff --git a/requirements.txt b/requirements.txt
index 8e191c11..2447817b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,61 +1,58 @@
-atari-py==0.1.7
-atomicwrites==1.3.0
-attrs==19.1.0
-bleach==3.1.0
-box2d-py==2.3.8
-certifi==2019.3.9
-chardet==3.0.4
-cloudpickle==0.8.0
-cymem==2.0.2
-cytoolz==0.9.0.1
-dill==0.2.9
+#
+# This file is autogenerated by pip-compile
+# To update, run:
+#
+#    pip-compile requirements.in
+#
+atari-py==0.2.6           # via gym
+atomicwrites==1.3.0       # via pytest
+attrs==19.3.0
+box2d-py==2.3.8           # via gym
+certifi==2019.9.11        # via requests
+chardet==3.0.4            # via requests
+cloudpickle==1.2.2
+cycler==0.10.0            # via matplotlib
 dnspython==1.16.0
-docutils==0.14
-future==0.17.1
-gym==0.12.0
-idna==2.8
-more-itertools==6.0.0
-msgpack==0.5.6
-msgpack-numpy==0.4.3.2
-murmurhash==1.0.2
-numpy==1.16.2
-opencv-python==4.0.0.21
-pandas==0.24.1
-Pillow==5.4.1
-pkginfo==1.5.0.1
-plac==0.9.6
-pluggy==0.9.0
-preshed==2.0.1
-py==1.8.0
-pyglet==1.3.2
-Pygments==2.3.1
-pymongo==3.7.2
-PyOpenGL==3.1.0
-pytest==4.3.1
-python-dateutil==2.8.0
-pytz==2018.9
-PyYAML==5.1
-pyzmq==18.0.1
-readme-renderer==24.0
-regex==2018.1.10
-requests==2.21.0
-requests-toolbelt==0.9.1
-scikit-learn==0.20.3
-scipy==1.2.1
-six==1.12.0
-spacy==2.0.18
-thinc==6.12.1
-toolz==0.9.0
-torch==1.0.1.post2
-torchfile==0.1.0
-torchtext==0.3.1
-torchvision==0.2.2.post3
-tornado==6.0.1
-tqdm==4.31.1
-twine==1.13.0
-ujson==1.35
-urllib3==1.24.2
-visdom==0.1.8.8
-webencodings==0.5.1
-websocket-client==0.55.0
-wrapt==1.10.11
+future==0.18.2            # via pyglet
+gym[atari,box2d,classic_control]==0.15.4
+idna==2.8                 # via requests
+importlib-metadata==0.23  # via pluggy, pytest
+joblib==0.14.0            # via scikit-learn
+jsonpatch==1.24           # via visdom
+jsonpointer==2.0          # via jsonpatch
+kiwisolver==1.1.0         # via matplotlib
+matplotlib==3.1.1
+more-itertools==7.2.0     # via pytest, zipp
+numpy==1.17.3
+opencv-python==4.1.1.26
+packaging==19.2           # via pytest
+pandas==0.25.3
+pillow==6.2.1             # via gym, torchvision, visdom
+pluggy==0.13.0            # via pytest
+py==1.8.0                 # via pytest
+pyglet==1.3.2             # via gym
+pymongo==3.9.0
+pyparsing==2.4.4          # via matplotlib, packaging
+pytest==5.2.2
+python-dateutil==2.8.1    # via matplotlib, pandas
+pytz==2019.3              # via pandas
+pyyaml==5.1.2
+pyzmq==18.1.0             # via visdom
+requests==2.22.0          # via torchtext, visdom
+scikit-learn==0.21.3
+scipy==1.3.2              # via gym, scikit-learn, visdom
+six==1.13.0               # via atari-py, cycler, gym, packaging, python-dateutil, torchtext, torchvision, visdom, websocket-client
+torch==1.3.1
+torchfile==0.1.0          # via visdom
+torchtext==0.4.0
+torchvision==0.4.2
+tornado==6.0.3            # via visdom
+tqdm==4.38.0
+urllib3==1.25.6           # via requests
+visdom==0.1.8.9
+wcwidth==0.1.7            # via pytest
+websocket-client==0.56.0  # via visdom
+zipp==0.6.0               # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools==41.6.0        # via kiwisolver
diff --git a/setup.py b/setup.py
index cb5f1854..cdad2f8d 100644
--- a/setup.py
+++ b/setup.py
@@ -16,7 +16,7 @@
 
 setup(
     name='vel',
-    version='0.3.0',
+    version='0.4.0',
     description="Velocity in deep-learning research",
     long_description=long_description,
     url='https://github.com/MillionIntegrals/vel',
@@ -28,24 +28,29 @@
     install_requires=[
         'attrs',
         'cloudpickle',
+        'matplotlib',
         'numpy',
         'opencv-python',
         'pandas',
         'pyyaml',
         'scikit-learn',
-        'torch ~= 1.0',
-        'torchvision',
+        'torch ~= 1.1',
         'torchtext',
+        'torchvision',
         'tqdm'
     ],
     extras_require={
         'visdom': ['visdom'],
+        'tensorboard': ['tb-nightly'],
         'mongo': ['pymongo', 'dnspython'],
         'gym': ['gym[atari,box2d,classic_control]'],
         'mujoco': ['gym[mujoco,robotics]'],
-        'dev': ['pytest', 'ipython', 'jupyter'],
+        'dev': ['pytest', 'ipython', 'jupyter', 'pip-tools', 'flake8', 'pytest-xdist'],
         'text': ['spacy'],
-        'all': ['visdom', 'pymongo', 'dnspython', 'gym[all]', 'pytest', 'spacy', 'ipython', 'jupyter']
+        'all': [
+            'visdom', 'pymongo', 'dnspython', 'gym[all]', 'pytest', 'spacy', 'ipython', 'jupyter',
+            'pip-tools', 'flake8', 'tb-nightly'
+        ]
     },
     tests_require=[
         'pytest'
diff --git a/vel/api/__init__.py b/vel/api/__init__.py
index ac1eec1f..4df70bd3 100644
--- a/vel/api/__init__.py
+++ b/vel/api/__init__.py
@@ -1,15 +1,15 @@
 from .callback import Callback
 from .info import BatchInfo, EpochInfo, TrainingInfo
-from .learner import Learner
+from .size_hint import SizeHint, SizeHints
+from .vmodule import VModule, BackboneModule
 from .model import (
-    Model, BackboneModel, LinearBackboneModel, SupervisedModel, RnnLinearBackboneModel, RnnModel, RnnSupervisedModel
+    Model, ValidatedModel, OptimizedModel, GradientModel, LossFunctionModel
 )
-from .model_factory import ModelFactory
-from .optimizer import OptimizerFactory
+from .model_config import ModelConfig
+from .model_factory import ModuleFactory
+from .optimizer import OptimizerFactory, VelOptimizer, VelOptimizerProxy
 from .schedule import Schedule
 from .scheduler import SchedulerFactory
-from .source import Source, TrainingData, TextData
+from .source import Source, LanguageSource
 from .storage import Storage
-from .train_phase import TrainPhase, EmptyTrainPhase
-
-from vel.internals.model_config import ModelConfig
+from .transformation import Transformation, ScopedTransformation
diff --git a/vel/api/callback.py b/vel/api/callback.py
index 6c28ad10..372ac699 100644
--- a/vel/api/callback.py
+++ b/vel/api/callback.py
@@ -1,3 +1,5 @@
+import typing
+
 from .info import EpochInfo, BatchInfo, TrainingInfo
 
 
@@ -13,10 +15,10 @@ def on_initialization(self, training_info: TrainingInfo) -> None:
         """
         pass
 
-    def on_train_begin(self, training_info: TrainingInfo) -> None:
+    def on_train_begin(self, training_info: TrainingInfo, model) -> None:
         """
         Beginning of a training process - is run every time a training process is started, even if it's restarted from
-        a checkpoint.
+        a checkpoint. Can access the model that is used for this training.
         """
         pass
 
@@ -38,30 +40,18 @@ def on_epoch_end(self, epoch_info: EpochInfo) -> None:
         """
         pass
 
-    def on_batch_begin(self, batch_info: BatchInfo) -> None:
+    def on_batch_begin(self, batch_info: BatchInfo, dataset: typing.Optional[str] = None) -> None:
         """
         Runs for each batch before batch is evaluated
         """
         pass
 
-    def on_batch_end(self, batch_info: BatchInfo) -> None:
+    def on_batch_end(self, batch_info: BatchInfo, dataset: typing.Optional[str] = None) -> None:
         """
         Runs for each batch after batch is evaluated
         """
         pass
 
-    def on_validation_batch_begin(self, batch_info: BatchInfo) -> None:
-        """
-        Supervised learning only - runs before validation batch
-        """
-        pass
-
-    def on_validation_batch_end(self, batch_info: BatchInfo) -> None:
-        """
-        Supervised learning only - runs after validation batch
-        """
-        pass
-
     def write_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict) -> None:
         """
         Persist callback state to the state dictionary
diff --git a/vel/api/data/__init__.py b/vel/api/data/__init__.py
deleted file mode 100644
index 76bebdab..00000000
--- a/vel/api/data/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .augmentation import Augmentation
-from .dataflow import DataFlow
-from .image_ops import *
\ No newline at end of file
diff --git a/vel/api/data/augmentation.py b/vel/api/data/augmentation.py
deleted file mode 100644
index b4b103de..00000000
--- a/vel/api/data/augmentation.py
+++ /dev/null
@@ -1,19 +0,0 @@
-
-
-class Augmentation:
-    """ Base class for all data augmentations """
-    def __init__(self, mode='x', tags=None):
-        self.mode = mode
-        self.tags = tags or ['train', 'val', 'test']
-
-    def __call__(self, *args):
-        """ Do the transformation """
-        print(self)
-        raise NotImplementedError
-
-    def denormalize(self, *args):
-        """ Operation reverse to normalization """
-        if len(args) == 1:
-            return args[0]
-        else:
-            return args
diff --git a/vel/api/data/dataflow.py b/vel/api/data/dataflow.py
deleted file mode 100644
index 6b880b4f..00000000
--- a/vel/api/data/dataflow.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import torch.utils.data as data
-
-
-class DataFlow(data.Dataset):
-    """ A dataset wrapping underlying data source with transformations """
-    def __init__(self, dataset, transformations, tag):
-        self.dataset = dataset
-
-        if transformations is None:
-            self.transformations = []
-        else:
-            self.transformations = [t for t in transformations if tag in t.tags]
-
-        self.tag = tag
-
-    def get_raw(self, index):
-        return self.dataset[index]
-
-    def __getitem__(self, index):
-        raw_x, raw_y = self.dataset[index]
-
-        for t in self.transformations:
-            if t.mode == 'x':
-                raw_x = t(raw_x)
-            elif t.mode == 'y':
-                raw_y = t(raw_y)
-            elif t.mode == 'both':
-                raw_x, raw_y = t(raw_x, raw_y)
-            else:
-                raise RuntimeError(f"Mode {t.mode} not recognized")
-
-        return raw_x, raw_y
-
-    def denormalize(self, datum, mode='x'):
-        for t in self.transformations[::-1]:
-            if t.mode == mode:
-                datum = t.denormalize(datum)
-
-        return datum
-
-    def __len__(self):
-        return len(self.dataset)
diff --git a/vel/api/info.py b/vel/api/info.py
index e4372b4e..48d25883 100644
--- a/vel/api/info.py
+++ b/vel/api/info.py
@@ -5,7 +5,7 @@
 
 import torch
 
-from vel.exceptions import VelException
+from vel.exception import VelException
 
 
 class TrainingHistory:
@@ -33,25 +33,19 @@ class TrainingInfo(abc.MutableMapping):
     Data dict is any extra information processes may want to store
     """
 
-    def __init__(self, start_epoch_idx=0, run_name: typing.Optional[str]=None, metrics=None, callbacks=None):
+    def __init__(self, start_epoch_idx=0, metrics=None, callbacks=None):
         self.data_dict = {}
 
         self.start_epoch_idx = start_epoch_idx
         self.metrics = metrics if metrics is not None else []
         self.callbacks = callbacks if callbacks is not None else []
-        self.run_name = run_name
         self.history = TrainingHistory()
 
-        self.optimizer_initial_state = None
-
     def restore(self, hidden_state):
         """ Restore any state from checkpoint - currently not implemented but possible to do so in the future """
         for callback in self.callbacks:
             callback.load_state_dict(self, hidden_state)
 
-        if 'optimizer' in hidden_state:
-            self.optimizer_initial_state = hidden_state['optimizer']
-
     def initialize(self):
         """
         Runs for the first time a training process is started from scratch. Is guaranteed to be run only once
@@ -60,13 +54,13 @@ def initialize(self):
         for callback in self.callbacks:
             callback.on_initialization(self)
 
-    def on_train_begin(self):
+    def on_train_begin(self, model):
         """
         Beginning of a training process - is run every time a training process is started, even if it's restarted from
         a checkpoint.
         """
         for callback in self.callbacks:
-            callback.on_train_begin(self)
+            callback.on_train_begin(self, model)
 
     def on_train_end(self):
         """
@@ -105,6 +99,15 @@ def __init__(self, global_epoch_idx, metrics):
         self._reset_metrics()
         self.metrics_by_name = {m.name: m for m in self.metrics}
 
+    def __contains__(self, metric):
+        if ':' in metric:
+            # TODO(jerry) There's got to be a better way to do it
+            metric_name = metric.split(':')[-1]
+        else:
+            metric_name = metric
+
+        return metric_name in self.metrics_by_name
+
     @torch.no_grad()
     def calculate(self, batch_info):
         """ Calculate metric values """
@@ -116,34 +119,28 @@ def _reset_metrics(self):
         for m in self.metrics:
             m.reset()
 
-    def value(self):
-        """ Return current value of the metrics """
-        return {m.name: m.value() for m in self.metrics}
+    def value(self, dataset=None):
+        """ Return current dictionary value of the metrics """
+        from vel.metric import MetricKey
+        return {MetricKey(m.name, m.scope, dataset, m.metric_type()): m.value() for m in self.metrics}
 
     def intermediate_value(self, metric):
         """ Return an intermediate (inter-epoch) value of a metric """
         if ':' in metric:
+            # TODO(jerry) There's got to be a better way to do it
             metric_name = metric.split(':')[-1]
         else:
             metric_name = metric
 
         return self.metrics_by_name[metric_name].value()
 
-    def freeze_results(self, name=None):
-        new_results = self.value()
-
-        if name is None:
-            for key, value in new_results.items():
-                self.frozen_results[key] = value
-        else:
-            for key, value in new_results.items():
-                self.frozen_results[f'{name}:{key}'] = value
-
+    def freeze_results(self, dataset=None):
+        self.frozen_results.update(self.value(dataset))
         self._reset_metrics()
 
     def result(self):
         """ Return the epoch result """
-        final_result = {'epoch_idx': self.global_epoch_idx}
+        final_result = {}
 
         for key, value in self.frozen_results.items():
             final_result[key] = value
@@ -162,7 +159,8 @@ class EpochInfo(abc.MutableMapping):
     """
 
     def __init__(self, training_info: TrainingInfo, global_epoch_idx: int, batches_per_epoch: int,
-                 optimizer: torch.optim.Optimizer=None, local_epoch_idx: int = None, callbacks: list=None):
+                 optimizer: typing.Optional[torch.optim.Optimizer] = None, local_epoch_idx: int = None,
+                 callbacks: typing.Optional[list] = None):
         self.training_info = training_info
         self.optimizer = optimizer
         self.batches_per_epoch = batches_per_epoch
@@ -259,28 +257,15 @@ def __init__(self, epoch_info: EpochInfo, batch_number: int):
         self.batch_number = batch_number
         self.data_dict = {}
 
-    def on_batch_begin(self):
-        """ Initialize batch processing """
-        for callback in self.callbacks:
-            callback.on_batch_begin(self)
-
-    def on_batch_end(self):
-        """ Finalize batch processing """
-        for callback in self.callbacks:
-            callback.on_batch_end(self)
-
-        # Even with all the experience replay, we count the single rollout as a single batch
-        self.epoch_info.result_accumulator.calculate(self)
-
-    def on_validation_batch_begin(self):
+    def on_batch_begin(self, dataset=None):
         """ Initialize batch processing """
         for callback in self.callbacks:
-            callback.on_validation_batch_begin(self)
+            callback.on_batch_begin(self, dataset)
 
-    def on_validation_batch_end(self):
+    def on_batch_end(self, dataset=None):
         """ Finalize batch processing """
         for callback in self.callbacks:
-            callback.on_validation_batch_end(self)
+            callback.on_batch_end(self, dataset)
 
         # Even with all the experience replay, we count the single rollout as a single batch
         self.epoch_info.result_accumulator.calculate(self)
@@ -348,4 +333,4 @@ def __contains__(self, item):
         return item in self.data_dict
 
     def __repr__(self):
-        return f"[BatchInfo epoch:{self.epoch_info.global_epoch_idx} batch:{self.batch_number}/{self.batches_per_epoch}]"
+        return f"[BatchInfo epoch:{self.epoch_info.global_epoch_idx} batch:{self.batch_number}/{self.batches_per_epoch}]"  # noqa
diff --git a/vel/api/learner.py b/vel/api/learner.py
deleted file mode 100644
index 98bb4b89..00000000
--- a/vel/api/learner.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import sys
-import torch
-import tqdm
-import typing
-
-from .info import BatchInfo, EpochInfo, TrainingInfo
-
-
-class Learner:
-    """ Manages training process of a single model """
-    def __init__(self, device: torch.device, model, max_grad_norm: typing.Optional[float]=None):
-        self.device = device
-        self.model = model.to(device)
-        self.max_grad_norm = max_grad_norm
-
-    def metrics(self):
-        """ Return metrics for given learner/model """
-        return self.model.metrics()
-
-    def summary(self):
-        """ Print summary for given learner/model """
-        return self.model.summary()
-
-    def train(self):
-        """ Set model in the training mode """
-        return self.model.train()
-
-    def eval(self):
-        """ Set model in the evaluation mode """
-        return self.model.eval()
-
-    def number_of_parameters(self):
-        """ Count model parameters """
-        return sum(p.numel() for p in self.model.parameters())
-
-    def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
-        """ Prepare for training """
-        if model_state is None:
-            self.model.reset_weights()
-        else:
-            self.model.load_state_dict(model_state)
-
-    def run_epoch(self, epoch_info: EpochInfo, source: 'vel.api.Source'):
-        """ Run full epoch of learning """
-        epoch_info.on_epoch_begin()
-
-        lr = epoch_info.optimizer.param_groups[-1]['lr']
-        print("|-------- Epoch {:06} Lr={:.6f} ----------|".format(epoch_info.global_epoch_idx, lr))
-
-        self.train_epoch(epoch_info, source)
-        epoch_info.result_accumulator.freeze_results('train')
-
-        self.validation_epoch(epoch_info, source)
-        epoch_info.result_accumulator.freeze_results('val')
-
-        epoch_info.on_epoch_end()
-
-    def train_epoch(self, epoch_info, source: 'vel.api.Source', interactive=True):
-        """ Run a single training epoch """
-        self.train()
-
-        if interactive:
-            iterator = tqdm.tqdm(source.train_loader(), desc="Training", unit="iter", file=sys.stdout)
-        else:
-            iterator = source.train_loader()
-
-        for batch_idx, (data, target) in enumerate(iterator):
-            batch_info = BatchInfo(epoch_info, batch_idx)
-
-            batch_info.on_batch_begin()
-            self.train_batch(batch_info, data, target)
-            batch_info.on_batch_end()
-
-            iterator.set_postfix(loss=epoch_info.result_accumulator.intermediate_value('loss'))
-
-    def validation_epoch(self, epoch_info, source: 'vel.api.Source'):
-        """ Run a single evaluation epoch """
-        self.eval()
-
-        iterator = tqdm.tqdm(source.val_loader(), desc="Validation", unit="iter", file=sys.stdout)
-
-        with torch.no_grad():
-            for batch_idx, (data, target) in enumerate(iterator):
-                batch_info = BatchInfo(epoch_info, batch_idx)
-
-                batch_info.on_validation_batch_begin()
-                self.feed_batch(batch_info, data, target)
-                batch_info.on_validation_batch_end()
-
-    def feed_batch(self, batch_info, data, target):
-        """ Run single batch of data """
-        data, target = data.to(self.device), target.to(self.device)
-        output, loss = self.model.loss(data, target)
-
-        # Store extra batch information for calculation of the statistics
-        batch_info['data'] = data
-        batch_info['target'] = target
-        batch_info['output'] = output
-        batch_info['loss'] = loss
-
-        return loss
-
-    def train_batch(self, batch_info, data, target):
-        """ Train single batch of data """
-        batch_info.optimizer.zero_grad()
-        loss = self.feed_batch(batch_info, data, target)
-        loss.backward()
-
-        if self.max_grad_norm is not None:
-            batch_info['grad_norm'] = torch.nn.utils.clip_grad_norm_(
-                filter(lambda p: p.requires_grad, self.model.parameters()),
-                max_norm=self.max_grad_norm
-            )
-
-        batch_info.optimizer.step()
diff --git a/vel/api/metrics/__init__.py b/vel/api/metrics/__init__.py
deleted file mode 100644
index f496db8a..00000000
--- a/vel/api/metrics/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base_metric import BaseMetric
-from .averaging_metric import AveragingMetric, AveragingNamedMetric, AveragingSupervisedMetric
-from .value_metric import ValueMetric
-from .summing_metric import SummingMetric, SummingNamedMetric
diff --git a/vel/api/model.py b/vel/api/model.py
index 53098d3e..259145ce 100644
--- a/vel/api/model.py
+++ b/vel/api/model.py
@@ -1,22 +1,23 @@
-import hashlib
 import torch
-import torch.nn as nn
 
 import vel.util.module_util as mu
 
-from vel.metrics.loss_metric import Loss
-from vel.util.summary import summary
+from vel.api.optimizer import VelOptimizer, OptimizerFactory
+from vel.metric.loss_metric import Loss
 
 
-class Model(nn.Module):
-    """ Class representing full neural network model """
+from .vmodule import VModule
+
+
+class Model(VModule):
+    """ Class representing full neural network model, generally used to solve some problem """
 
     def metrics(self) -> list:
         """ Set of metrics for this model """
-        return [Loss()]
+        return []
 
     def train(self, mode=True):
-        r"""
+        """
         Sets the module in training mode.
 
         This has any effect only on certain modules. See documentations of
@@ -34,126 +35,105 @@ def train(self, mode=True):
 
         return self
 
-    def summary(self, input_size=None, hashsummary=False):
+    def summary(self):
         """ Print a model summary """
+        print(self)
+        print("-" * 100)
+        for name, module in self.named_parameters():
+            print("> {} {:,}".format(name, module.numel()))
+        print("-" * 100)
+        number = sum(p.numel() for p in self.parameters())
+        print("Number of model parameters: {:,}".format(number))
+        print("-" * 100)
 
-        if input_size is None:
-            print(self)
-            print("-" * 120)
-            number = sum(p.numel() for p in self.model.parameters())
-            print("Number of model parameters: {:,}".format(number))
-            print("-" * 120)
-        else:
-            summary(self, input_size)
-
-        if hashsummary:
-            for idx, hashvalue in enumerate(self.hashsummary()):
-                print(f"{idx}: {hashvalue}")
-
-    def hashsummary(self):
-        """ Print a model summary - checksums of each layer parameters """
-        children = list(self.children())
-
-        result = []
-
-        for child in children:
-            result.extend(hashlib.sha256(x.detach().cpu().numpy().tobytes()).hexdigest() for x in child.parameters())
 
-        return result
+class OptimizedModel(Model):
+    """ Model that is being optimized by an 'optimizer' """
 
-    def get_layer_groups(self):
-        """ Return layers grouped """
-        return [self]
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        parameters = filter(lambda p: p.requires_grad, self.parameters())
+        return optimizer_factory.instantiate(parameters)
 
-    def reset_weights(self):
-        """ Call proper initializers for the weights """
-        pass
-
-    @property
-    def is_recurrent(self) -> bool:
-        """ If the network is recurrent and needs to be fed state as well as the observations """
-        return False
-
-
-class RnnModel(Model):
-    """ Class representing recurrent model """
-
-    @property
-    def is_recurrent(self) -> bool:
-        """ If the network is recurrent and needs to be fed previous state """
-        return True
-
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
+    def optimize(self, data: dict, optimizer: VelOptimizer) -> dict:
+        """
+        Perform one step of optimization of the model
+        :returns a dictionary of metrics
+        """
         raise NotImplementedError
 
-    def zero_state(self, batch_size):
-        """ Initial state of the network """
-        return torch.zeros(batch_size, self.state_dim)
 
+class ValidatedModel(OptimizedModel):
+    """ Model that also has a validation operation """
 
-class BackboneModel(Model):
-    """ Model that serves as a backbone network to connect your heads to """
+    def validate(self, data: dict) -> dict:
+        """
+        Perform one step of model inference without optimization
+        :returns a dictionary of metrics
+        """
+        raise NotImplementedError
 
 
-class RnnLinearBackboneModel(BackboneModel):
-    """
-    Model that serves as a backbone network to connect your heads to -
-    one that spits out a single-dimension output and is a recurrent neural network
-    """
+class GradientModel(ValidatedModel):
+    """ Model that calculates a single gradient and optimizes it """
 
-    @property
-    def is_recurrent(self) -> bool:
-        """ If the network is recurrent and needs to be fed previous state """
-        return True
+    def optimize(self, data: dict, optimizer: VelOptimizer) -> dict:
+        """
+        Perform one step of optimization of the model
+        :returns a dictionary of metrics
+        """
+        optimizer.zero_grad()
 
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        raise NotImplementedError
+        metrics = self.calculate_gradient(data)
 
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
-        raise NotImplementedError
+        opt_metrics = optimizer.step()
 
-    def zero_state(self, batch_size):
-        """ Initial state of the network """
-        return torch.zeros(batch_size, self.state_dim, dtype=torch.float32)
+        for key, value in opt_metrics.items():
+            metrics[key] = value
 
+        return metrics
 
-class LinearBackboneModel(BackboneModel):
-    """
-    Model that serves as a backbone network to connect your heads to - one that spits out a single-dimension output
-    """
+    @torch.no_grad()
+    def validate(self, data: dict) -> dict:
+        """
+        Perform one step of model inference without optimization
+        :returns a dictionary of metrics
+        """
+        return self.calculate_gradient(data)
 
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
+    def calculate_gradient(self, data: dict) -> dict:
+        """
+        Calculate gradient for given batch of training data.
+        :returns a dictionary of metrics
+        """
         raise NotImplementedError
 
 
-class SupervisedModel(Model):
-    """ Model for a supervised learning problem """
-    def loss(self, x_data, y_true):
-        """ Forward propagate network and return a value of loss function """
-        y_pred = self(x_data)
-        return y_pred, self.loss_value(x_data, y_true, y_pred)
+class LossFunctionModel(GradientModel):
+    """ Model for a supervised learning with a simple loss function """
 
-    def loss_value(self, x_data, y_true, y_pred):
-        """ Calculate a value of loss function """
-        raise NotImplementedError
+    def metrics(self) -> list:
+        """ Set of metrics for this model """
+        return [Loss()]
+
+    def calculate_gradient(self, data: dict) -> dict:
+        if self.is_stateful:
+            y_hat, _ = self(data['x'])
+        else:
+            y_hat = self(data['x'])
 
+        loss_value = self.loss_value(data['x'], data['y'], y_hat)
 
-class RnnSupervisedModel(RnnModel):
-    """ Model for a supervised learning problem """
+        if self.training:
+            loss_value.backward()
 
-    def loss(self, x_data, y_true):
-        """ Forward propagate network and return a value of loss function """
-        y_pred = self(x_data)
-        return y_pred, self.loss_value(x_data, y_true, y_pred)
+        return {
+            'loss': loss_value.item(),
+            'data': data['x'],
+            'target': data['y'],
+            'output': y_hat
+        }
 
-    def loss_value(self, x_data, y_true, y_pred):
+    def loss_value(self, x_data, y_true, y_pred) -> torch.tensor:
         """ Calculate a value of loss function """
         raise NotImplementedError
diff --git a/vel/api/model_config.py b/vel/api/model_config.py
new file mode 100644
index 00000000..a33dc9f7
--- /dev/null
+++ b/vel/api/model_config.py
@@ -0,0 +1,344 @@
+import datetime as dtm
+import json
+import os.path
+import pathlib
+import typing
+
+from vel.exception import VelInitializationException
+from vel.internal.parser import Parser
+from vel.internal.provider import Provider
+
+from .info import TrainingInfo
+
+
+class ModelConfig:
+    """
+    Read from YAML configuration of a model, specifying all details of the run.
+    Is a frontend for the provider, resolving all dependency-injection requests.
+    """
+
+    PROJECT_FILE_NAME = '.velproject.yaml'
+    META_FILE_NAME = 'meta.json'
+
+    @staticmethod
+    def find_project_directory(start_path) -> str:
+        """ Locate top-level project directory  """
+        start_path = os.path.realpath(start_path)
+        possible_name = os.path.join(start_path, ModelConfig.PROJECT_FILE_NAME)
+
+        if os.path.exists(possible_name):
+            return start_path
+        else:
+            up_path = os.path.realpath(os.path.join(start_path, '..'))
+            if os.path.realpath(start_path) == up_path:
+                raise RuntimeError(f"Couldn't find project file starting from {start_path}")
+            else:
+                return ModelConfig.find_project_directory(up_path)
+
+    @staticmethod
+    def from_project_directory(path) -> str:
+        """ Locate given path relative to project directory """
+        return os.path.join(ModelConfig.find_project_directory('.'), path)
+
+    @classmethod
+    def from_file(cls, filename: str, run_number: int = 1, resume_training: bool = False, seed: int = None,
+                  device: str = 'cuda', parameters: typing.Optional[dict] = None, tag: typing.Optional[str] = None):
+        """ Create model config from file """
+        with open(filename, 'r') as fp:
+            model_config_contents = Parser.parse(fp)
+
+        project_config_path = ModelConfig.find_project_directory(os.path.dirname(os.path.abspath(filename)))
+
+        with open(os.path.join(project_config_path, cls.PROJECT_FILE_NAME), 'r') as fp:
+            project_config_contents = Parser.parse(fp)
+
+        aggregate_dictionary = {
+            **project_config_contents,
+            **model_config_contents
+        }
+
+        return ModelConfig(
+            filename=filename,
+            configuration=aggregate_dictionary,
+            run_number=run_number,
+            project_dir=project_config_path,
+            resume_training=resume_training,
+            seed=seed,
+            device=device,
+            parameters=parameters,
+            tag=tag
+        )
+
+    @classmethod
+    def script(cls, model_name: str = 'script', configuration: typing.Optional[dict] = None, run_number: int = 1,
+               resume_training=False, seed: int = None, device: str = 'cuda',
+               parameters: typing.Optional[dict] = None, tag: typing.Optional[str] = None):
+        """ Create model config from supplied data """
+        if configuration is None:
+            configuration = {}
+
+        configuration['name'] = model_name
+
+        project_config_path = ModelConfig.find_project_directory(os.path.dirname(os.path.abspath(os.getcwd())))
+
+        with open(os.path.join(project_config_path, cls.PROJECT_FILE_NAME), 'r') as fp:
+            project_config_contents = Parser.parse(fp)
+
+        aggregate_dictionary = {
+            **project_config_contents,
+            **configuration
+        }
+
+        return ModelConfig(
+            filename="[script]",
+            configuration=aggregate_dictionary,
+            run_number=run_number,
+            project_dir=project_config_path,
+            resume_training=resume_training,
+            seed=seed,
+            device=device,
+            parameters=parameters,
+            tag=tag
+        )
+
+    def __init__(self, filename: str, configuration: dict, run_number: int, project_dir: str,
+                 resume_training=False, seed: int = None, device: str = 'cuda',
+                 parameters: typing.Optional[dict] = None, tag: typing.Optional[str] = None):
+        self.filename = filename
+        self.device = device
+        self.resume_training = resume_training
+        self.run_number = run_number
+        self.seed = seed if seed is not None else (dtm.date.today().year + self.run_number)
+
+        self.contents = configuration
+        self.project_dir = os.path.normpath(project_dir)
+
+        self.command_descriptors = {
+            **self.contents.get('global_commands', {}),
+            **self.contents.get('commands', {})
+        }
+
+        # This one is special and needs to get removed
+        if 'commands' in self.contents:
+            del self.contents['commands']
+
+        if 'global_commands' in self.contents:
+            del self.contents['global_commands']
+
+        self.provider = Provider(self._prepare_environment(), {'model_config': self}, parameters=parameters)
+
+        if self.provider.has_name('output_directory'):
+            self.output_directory_name = self.provider.get("output_directory")
+        else:
+            self.output_directory_name = 'output'
+
+        self._model_name = self.provider.get("name")
+
+        if self.meta_exists():
+            self._meta = self._load_meta()
+
+            if resume_training:
+                if (tag is not None) and (tag != self._meta['tag']):
+                    raise VelInitializationException("Model tag mismatch")
+                else:
+                    self._tag = self._meta['tag']
+            else:
+                self._tag = tag
+        else:
+            self._tag = tag
+            self._meta = None
+
+    ####################################################################################################################
+    # INTERNAL FUNCTIONS
+    def _prepare_environment(self) -> dict:
+        """ Return full environment for dependency injection """
+        return {**self.contents, 'run_number': self.run_number}
+
+    def _load_meta(self) -> dict:
+        """ Load previously written metadata about the project """
+        if not self.meta_exists():
+            raise VelInitializationException("Previous run does not exist")
+
+        with open(self.meta_dir(self.META_FILE_NAME), 'rt') as fp:
+            return json.load(fp)
+
+    def _create_meta(self) -> dict:
+        """ Metadata for this model/config """
+        return {
+            'run_name': self.run_name,
+            'tag': self.tag,
+            'created': dtm.datetime.now().strftime("%Y/%m/%d - %H:%M:%S"),
+            'config': self.render_configuration()
+        }
+
+    ####################################################################################################################
+    # Metadata handling
+    def meta_exists(self):
+        """ If metadata file exists for this config """
+        return os.path.exists(self.meta_dir(self.META_FILE_NAME))
+
+    def enforce_meta(self):
+        """ Make sure metadata exists for this config """
+        if self._meta is None:
+            raise VelInitializationException("Given model has not been initialized")
+
+    def write_meta(self) -> None:
+        """ Write metadata to a file """
+        self._meta = self._create_meta()
+
+        pathlib.Path(self.meta_dir()).mkdir(parents=True, exist_ok=True)
+
+        with open(self.meta_dir(self.META_FILE_NAME), 'wt') as fp:
+            return json.dump(self.meta, fp)
+
+    ####################################################################################################################
+    # COMMAND UTILITIES
+    def get_command(self, command_name):
+        """ Return object for given command """
+        return self.provider.instantiate_from_data(self.command_descriptors[command_name])
+
+    def run_command(self, command_name, varargs=None):
+        """ Instantiate model class """
+        if varargs is None:
+            varargs = []
+
+        command_descriptor = self.get_command(command_name)
+        return command_descriptor.run(*varargs)
+
+    ####################################################################################################################
+    # MODEL DIRECTORIES
+    def project_top_dir(self, *args) -> str:
+        """ Project top-level directory """
+        return os.path.join(self.project_dir, *args)
+
+    def output_dir(self, *args) -> str:
+        """ Directory where to store output """
+        return os.path.join(self.project_dir, self.output_directory_name, *args)
+
+    def meta_dir(self, *args) -> str:
+        """ Return directory for metadata output files for this model """
+        return self.output_dir('meta', self.run_name, *args)
+
+    def data_dir(self, *args) -> str:
+        """ Directory where to store data """
+        return os.path.normpath(os.path.join(self.project_dir, 'data', *args))
+
+    def model_output_dir(self, *args):
+        """ Return an output directory of given kind for given kind only """
+        fname = os.path.join(self.project_dir, self.output_directory_name, args[0], self.run_name, *(args[1:]))
+        os.makedirs(os.path.dirname(fname), exist_ok=True)
+        return fname
+
+    def checkpoint_dir(self, *args) -> str:
+        """ Return checkpoint directory for this model """
+        return self.output_dir('checkpoints', self.run_name, *args)
+
+    def openai_dir(self, *args) -> str:
+        """ Return directory for openai output files for this model """
+        return self.output_dir('openai', self.run_name, *args)
+    ####################################################################################################################
+    # NAME UTILITIES
+    @property
+    def run_name(self) -> str:
+        """ Return name of the run """
+        return "{}/{}".format(self._model_name, self.run_number)
+
+    @property
+    def name(self) -> str:
+        """ Return name of the model """
+        return self._model_name
+
+    @property
+    def meta(self) -> dict:
+        """ Return name of the model """
+        self.enforce_meta()
+        return self._meta
+
+    @property
+    def tag(self) -> typing.Optional[str]:
+        """ Tag for this model/run number """
+        return self._tag
+
+    ####################################################################################################################
+    # MISC GETTERS
+    def torch_device(self):
+        """ Return torch device object """
+        import torch
+        return torch.device(self.device)
+
+    def render_configuration(self) -> dict:
+        """ Return a nice and picklable run configuration """
+        return self.provider.render_environment()
+
+    ####################################################################################################################
+    # PROVIDER API
+    def provide(self, name):
+        """ Return a dependency-injected instance """
+        return self.provider.instantiate_by_name(name)
+
+    def provide_with_default(self, name, default=None):
+        """ Return a dependency-injected instance """
+        return self.provider.instantiate_by_name_with_default(name, default_value=default)
+
+    ####################################################################################################################
+    # BANNERS - Maybe shouldn't be here, but they are for now
+    def banner(self, command_name) -> None:
+        """ Print a banner for running the system """
+        import torch
+        device = self.torch_device()
+
+        print("=" * 80)
+        print(f"Pytorch version: {torch.__version__} cuda version {torch.version.cuda} cudnn version {torch.backends.cudnn.version()}")  # noqa
+
+        if self.tag:
+            print("Running model {}, run {} ({}) -- command {} -- device {}".format(
+                self._model_name, self.run_number, self.tag, command_name, self.device)
+            )
+        else:
+            print("Running model {}, run {} -- command {} -- device {}".format(
+                self._model_name, self.run_number, command_name, self.device)
+            )
+
+        if device.type == 'cuda':
+            device_idx = 0 if device.index is None else device.index
+            print(f"CUDA Device name {torch.cuda.get_device_name(device_idx)}")
+        print(dtm.datetime.now().strftime("%Y/%m/%d - %H:%M:%S"))
+        print("=" * 80)
+
+    def quit_banner(self) -> None:
+        """ Print a banner for running the system """
+        print("=" * 80)
+        print("Done.")
+        print(dtm.datetime.now().strftime("%Y/%m/%d - %H:%M:%S"))
+        print("=" * 80)
+
+    ####################################################################################################################
+    # Small UI utils
+    def __repr__(self):
+        return f"<ModelConfig at {self.filename}>"
+
+    ####################################################################################################################
+    # CONVENIENCE METHODS FOR SCRIPTS
+    def load_trained_model(self):
+        """ Load a latest trained model from storage """
+        model = self.provide("model").instantiate()
+        storage = self.provide("storage")
+
+        last_epoch_idx = storage.last_epoch_idx()
+
+        if last_epoch_idx == 0:
+            raise VelInitializationException("No trained model available")
+
+        training_info = TrainingInfo(start_epoch_idx=last_epoch_idx)
+
+        model_state, hidden_state = storage.load(training_info)
+
+        model.load_state_dict(model_state)
+
+        return model
+
+    def set_seed(self):
+        """ Set random seeds """
+        # Set seed already in the launcher
+        from vel.util.random import set_seed
+        set_seed(self.seed)
diff --git a/vel/api/model_factory.py b/vel/api/model_factory.py
index e2e61896..2920877c 100644
--- a/vel/api/model_factory.py
+++ b/vel/api/model_factory.py
@@ -1,15 +1,15 @@
-from .model import Model
-from vel.internals.generic_factory import GenericFactory
+from vel.internal.generic_factory import GenericFactory
+from .vmodule import VModule
 
 
-class ModelFactory:
-    """ Factory class for models """
+class ModuleFactory:
+    """ Factory for modules """
 
-    def instantiate(self, **extra_args) -> Model:
+    def instantiate(self, **extra_args) -> VModule:
         raise NotImplementedError
 
     @staticmethod
-    def generic(closure, **kwargs) -> 'ModelFactory':
+    def generic(closure, **kwargs) -> 'ModuleFactory':
         """ Return a generic model factory """
         # noinspection PyTypeChecker
         return GenericFactory(closure, kwargs)
diff --git a/vel/api/optimizer.py b/vel/api/optimizer.py
index 0f677f19..4b926f53 100644
--- a/vel/api/optimizer.py
+++ b/vel/api/optimizer.py
@@ -1,10 +1,266 @@
-from vel.api import Model
+import collections
+import itertools as it
+import typing
 
-from torch.optim import Optimizer
+from torch.nn.utils import clip_grad_norm_
+from torch.optim.optimizer import Optimizer
+
+from vel.api.callback import Callback
+from vel.api.scheduler import SchedulerFactory
+from vel.exception import VelException
+from vel.metric import DefaultAveragingNamedMetric
+from vel.util.datastructure import flatten_dict
+
+
+class VelOptimizer:
+    """ Vel optimizer interface """
+
+    def get_lr(self) -> float:
+        """ Return current learning rate of the optimizer """
+        raise NotImplementedError
+
+    def set_lr(self, lr: float):
+        """ Set current learning rate of the optimizer """
+        raise NotImplementedError
+
+    def state_dict(self) -> dict:
+        raise NotImplementedError
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        raise NotImplementedError
+
+    def zero_grad(self) -> None:
+        raise NotImplementedError
+
+    def step(self, closure=None) -> dict:
+        raise NotImplementedError
+
+    def add_param_group(self, param_group: dict) -> None:
+        raise NotImplementedError
+
+    def metrics(self) -> list:
+        """ Set of metrics for this model """
+        return []
+
+    def create_scheduler(self, scheduler_factory: SchedulerFactory, last_epoch: int = -1) -> [Callback]:
+        """ Create a scheduler instance for this optimizer """
+        raise NotImplementedError
+
+
+class VelOptimizerProxy(VelOptimizer):
+    """ Proxy PyTorch optimizer into a Vel optimizer """
+    def __init__(self, optimizer: Optimizer, group_names: [str], max_grad_norm: typing.Optional[float] = None):
+        self.optimizer = optimizer
+        self.group_names = group_names
+        self.max_grad_norm = max_grad_norm
+
+        if 'default' in self.group_names:
+            self.main_idx = self.group_names.index('default')
+        else:
+            self.main_idx = len(self.group_names) - 1
+
+        assert len(self.optimizer.param_groups) == len(self.group_names), \
+            "There must be equal number of parameter groups and group names"
+
+        self.initial_lrs = [x['lr'] for x in self.optimizer.param_groups]
+
+    def get_lr(self) -> float:
+        """ Return current learning rate of the optimizer """
+        return self.optimizer.param_groups[self.main_idx]['lr']
+
+    def set_lr(self, lr: float):
+        """ Set current learning rate of the optimizer """
+        if isinstance(lr, list):
+            for group_lr, param_group in zip(lr, self.optimizer.param_groups):
+                param_group['lr'] = group_lr
+        elif isinstance(lr, dict):
+            for idx, name in enumerate(self.group_names):
+                self.optimizer.param_groups[idx]['lr'] = lr[name]
+        else:
+            canonical_lr = self.initial_lrs[0]
+
+            for idx, param_group in enumerate(self.optimizer.param_groups):
+                opt_lr = self.initial_lrs[idx] / canonical_lr * lr
+                param_group['lr'] = opt_lr
+
+    def state_dict(self) -> dict:
+        return self.optimizer.state_dict()
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        self.optimizer.load_state_dict(state_dict)
+
+    def zero_grad(self) -> None:
+        self.optimizer.zero_grad()
+
+    def step(self, closure=None) -> dict:
+        # TODO(jerry): potentially allow max_grad_norm being a list?
+        if self.max_grad_norm is not None:
+            grad_norm = clip_grad_norm_(
+                parameters=it.chain.from_iterable(pg['params'] for pg in self.optimizer.param_groups),
+                max_norm=self.max_grad_norm
+            )
+            self.optimizer.step(closure)
+            return {'grad_norm': grad_norm}
+        else:
+            self.optimizer.step(closure)
+            return {}
+
+    def add_param_group(self, param_group: dict) -> None:
+        self.optimizer.add_param_group(param_group)
+
+    def metrics(self) -> list:
+        """ Set of metrics for this model """
+        if self.max_grad_norm is not None:
+            return [
+                DefaultAveragingNamedMetric('grad_norm', scope="opt", defaut_value=0.0)
+            ]
+        else:
+            return []
+
+    def create_scheduler(self, scheduler_factory: SchedulerFactory, last_epoch: int = -1) -> [Callback]:
+        """ Create a scheduler instance for this optimizer """
+        return [scheduler_factory.instantiate(self.optimizer, last_epoch=last_epoch)]
+
+
+class VelMultiOptimizer(VelOptimizer):
+    """ Optimizer that wraps several individual optimizers """
+
+    def __init__(self, optimizers: typing.Dict[str, VelOptimizer], canonical_name: typing.Optional[str] = None):
+        self.optimizers = optimizers
+
+        # Canonical, chosen optimizer
+        if canonical_name is None:
+            self.canonical_name = list(optimizers.keys())[0]
+        else:
+            self.canonical_name = canonical_name
+
+        self.initial_lrs = {
+            name: optimizer.get_lr()
+            for name, optimizer in self.optimizers.items()
+        }
+
+    def __getitem__(self, item):
+        return self.optimizers[item]
+
+    def get_lr(self) -> float:
+        return self.optimizers[self.canonical_name].get_lr()
+
+    def set_lr(self, lr: float):
+        if isinstance(lr, list):
+            # TODO: implement
+            raise NotImplementedError
+        elif isinstance(lr, dict):
+            # TODO: implement
+            raise NotImplementedError
+        else:
+            canonical_lr = self.initial_lrs[self.canonical_name]
+
+            for name, optimizer in self.optimizers.items():
+                opt_lr = self.initial_lrs[name] / canonical_lr * lr
+                optimizer.set_lr(opt_lr)
+
+    def state_dict(self) -> dict:
+        output = {}
+
+        for name, optimizer in self.optimizers.items():
+            output[name] = optimizer.state_dict()
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        for name, optimizer in self.optimizers.items():
+            optimizer.load_state_dict(state_dict[name])
+
+    def zero_grad(self) -> None:
+        for optimizer in self.optimizers.values():
+            optimizer.zero_grad()
+
+    def step(self, closure=None) -> dict:
+        output = {}
+
+        for name, optimizer in self.optimizers.items():
+            metrics = optimizer.step()
+            output[name] = metrics
+
+        return output
+
+    def aggregate_metrics(self, metrics) -> dict:
+        """ Aggregate metrics from multiple optimizers """
+        output = {}
+
+        for key, value in metrics.items():
+            flatten_dict(value, output, key)
+
+        return output
+
+    def create_scheduler(self, scheduler_factory: SchedulerFactory, last_epoch: int = -1) -> [Callback]:
+        """ Create a scheduler instance for this optimizer """
+        return [
+            scheduler_factory.instantiate(optimizer, last_epoch=last_epoch)
+            for optimizer in self.optimizers.values()
+        ]
+
+    def add_param_group(self, param_group: dict) -> None:
+        raise VelException("Unsupported operation")
+
+    def metrics(self) -> list:
+        """ Set of metrics for this model """
+        # TODO(jerry): aggregate metrics
+        return [
+            metric.prefix(name)
+            for name, optimizer in self.optimizers.items()
+            for metric in optimizer.metrics()
+        ]
 
 
 class OptimizerFactory:
     """ Base class for optimizer factories """
+    def __init__(self):
+        self.parameter_groups = None
+
+    def with_parameter_groups(self, parameter_groups=None):
+        """ Set `parameter_groups` for this factory """
+        self.parameter_groups = parameter_groups
+        return self
+
+    def preprocess(self, parameters):
+        """ Preprocess given parameters input into proper optimizer parameter groups, with their names """
+        parameters = list(parameters)
+
+        # Make sure parameters have right format
+        if parameters:
+            if not isinstance(parameters[0], collections.Sequence) or not isinstance(parameters[0][0], str):
+                parameters = [("default", parameters)]
+
+        groups = collections.defaultdict(list)
+
+        for name, group in parameters:
+            group = [x for x in group if x.requires_grad]
+            if group:  # Must have at least 1 element
+                groups[name].extend(group)
 
-    def instantiate(self, model: Model) -> Optimizer:
+        group_names = []
+        sorted_groups = []
+
+        for name in sorted(groups.keys()):
+            parameter_group = {
+                'params': groups[name]
+            }
+
+            if self.parameter_groups and name in self.parameter_groups:
+                parameter_group.update(self.parameter_groups[name])
+
+            sorted_groups.append(parameter_group)
+            group_names.append(name)
+
+        return sorted_groups, group_names
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        """ Instantiate VelOptimizer for iterable of parameters or iterable of (parameter, group) """
         raise NotImplementedError
+
+    def instantiate_multi(self, parameter_dict: dict) -> VelMultiOptimizer:
+        od = collections.OrderedDict()
+
+        for name, value in parameter_dict.items():
+            od[name] = self.instantiate(value)
+
+        return VelMultiOptimizer(od)
diff --git a/vel/api/size_hint.py b/vel/api/size_hint.py
new file mode 100644
index 00000000..c8e687a8
--- /dev/null
+++ b/vel/api/size_hint.py
@@ -0,0 +1,91 @@
+import typing
+import collections.abc as abc
+
+from vel.exception import VelException
+
+
+class SizeHint(tuple):
+    """ Neural network hint of a layer size. Should consist of either integers or Nones """
+
+    def __new__(cls, *args):
+        return super().__new__(cls, tuple(args))
+
+    def last(self) -> int:
+        """ Return last part of the size hint, make sure it's not None """
+        assert self[-1] is not None, "Size hint shouldn't be None"
+        return self[-1]
+
+    def shape(self, idx=1) -> typing.Tuple[int]:
+        """ Get shape of size hint, except for a number of dimensions (batch dimensions """
+        return self[idx:]
+
+    def append(self, element: int) -> 'SizeHint':
+        """ Return a copy of this size hint, with new element added """
+        return SizeHint(*(list(self) + [element]))
+
+    def drop_last(self) -> 'SizeHint':
+        """ Return a copy of this size hint, with last element dropped """
+        return SizeHint(*self[:-1])
+
+    def __repr__(self):
+        internal = ", ".join([self._inner_repr(s) for s in self])
+        return f"{self.__class__.__name__}({internal})"
+
+    def _inner_repr(self, x):
+        if x is None:
+            return '-'
+        else:
+            return repr(x)
+
+
+SizeTuple = typing.Tuple[SizeHint]
+SizeDict = typing.Dict[str, SizeHint]
+
+
+class SizeHints:
+    """ SizeHint, tuple of size hints or dict of size hints """
+
+    TYPE_NONE = 0
+    TYPE_SIZE = 1
+    TYPE_TUPLE = 2
+    TYPE_DICT = 3
+
+    def __init__(self, size_hints: typing.Union[SizeHint, SizeTuple, SizeDict] = None):
+        self.size_hints = size_hints
+
+        if self.size_hints is None:
+            self.type = self.TYPE_NONE
+        elif isinstance(self.size_hints, SizeHint):
+            self.type = self.TYPE_SIZE
+        elif isinstance(self.size_hints, abc.Sequence):
+            self.size_hints = tuple(self.size_hints)
+            self.type = self.TYPE_TUPLE
+        elif isinstance(self.size_hints, abc.Mapping):
+            self.type = self.TYPE_DICT
+        else:
+            raise VelException("Invalid size hints: {}".format(self.size_hints))
+
+    def assert_tuple(self, length: typing.Optional[int] = None) -> SizeTuple:
+        """ Assert given size hints is a tuple """
+        assert self.type == self.TYPE_TUPLE, "Network needs to return a tuple"
+
+        if length is not None:
+            assert len(self.size_hints) == length, "Network must return {} results".format(length)
+
+        return self.size_hints
+
+    def assert_single(self, length: typing.Optional[int] = None) -> SizeHint:
+        """ Make sure there is a single tensor as a size hint """
+        assert self.type == self.TYPE_SIZE, "Layer input must be single tensor"
+
+        if length is not None:
+            assert len(self.size_hints) == length, f"Layer input must have shape [{length}]"
+
+        return self.size_hints
+
+    def unwrap(self):
+        """ Return the underlying data """
+        return self.size_hints
+
+    def __repr__(self):
+        return repr(self.size_hints)
diff --git a/vel/api/source.py b/vel/api/source.py
index 083221ee..892f65a7 100644
--- a/vel/api/source.py
+++ b/vel/api/source.py
@@ -1,120 +1,74 @@
+import typing
+
 import torch.utils.data as data
 
 
 class Source:
-    """ Source of data for supervised learning algorithms """
-    def __init__(self):
-        pass
-
-    def train_loader(self):
-        """ PyTorch loader of training data """
-        raise NotImplementedError
-
-    def val_loader(self):
-        """ PyTorch loader of validation data """
-        raise NotImplementedError
-
-    def train_dataset(self):
-        """ Return the training dataset """
-        raise NotImplementedError
-
-    def val_dataset(self):
-        """ Return the validation dataset """
-        raise NotImplementedError
-
-    def train_iterations_per_epoch(self):
-        """ Return number of iterations per epoch """
-        raise NotImplementedError
-
-    def val_iterations_per_epoch(self):
-        """ Return number of iterations per epoch - validation """
-        raise NotImplementedError
-
-
-class TextData(Source):
-    """ An NLP torchtext data source """
-    def __init__(self, train_source, val_source, train_iterator, val_iterator, data_field, target_field):
-        super().__init__()
-
-        self.train_source = train_source
-        self.val_source = val_source
-        self.train_iterator = train_iterator
-        self.val_iterator = val_iterator
-        self.data_field = data_field
-        self.target_field = target_field
-
-    def train_loader(self):
-        """ PyTorch loader of training data """
-        return self.train_iterator
-
-    def val_loader(self):
-        """ PyTorch loader of validation data """
-        return self.val_iterator
-
-    def train_dataset(self):
-        """ Return the training dataset """
-        return self.train_source
-
-    def val_dataset(self):
-        """ Return the validation dataset """
-        return self.val_source
-
-    def train_iterations_per_epoch(self):
-        """ Return number of iterations per epoch """
-        return len(self.train_iterator)
-
-    def val_iterations_per_epoch(self):
-        """ Return number of iterations per epoch - validation """
-        return len(self.val_iterator)
-
-
-class TrainingData(Source):
-    """ Most common source of data combining a basic datasource and sampler """
-    def __init__(self, train_source, val_source, num_workers, batch_size, augmentations=None):
-        import vel.api.data as vel_data
-
-        super().__init__()
-
-        self.train_source = train_source
-        self.val_source = val_source
-
-        self.num_workers = num_workers
-        self.batch_size = batch_size
-
-        self.augmentations = augmentations
-
-        # Derived values
-        self.train_ds = vel_data.DataFlow(self.train_source, augmentations, tag='train')
-        self.val_ds = vel_data.DataFlow(self.val_source, augmentations, tag='val')
-
-        self._train_loader = data.DataLoader(
-            self.train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers
-        )
-
-        self._val_loader = data.DataLoader(
-            self.val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers
-        )
-
-    def train_loader(self):
-        """ PyTorch loader of training data """
-        return self._train_loader
-
-    def val_loader(self):
-        """ PyTorch loader of validation data """
-        return self._val_loader
-
-    def train_dataset(self):
-        """ Return the training dataset """
-        return self.train_ds
-
-    def val_dataset(self):
-        """ Return the validation dataset """
-        return self.val_ds
-
-    def train_iterations_per_epoch(self):
-        """ Return number of iterations per epoch """
-        return len(self._train_loader)
-
-    def val_iterations_per_epoch(self):
-        """ Return number of iterations per epoch - validation """
-        return len(self._val_loader)
+    """
+    Single simple container for train/validation/test datasets.
+
+    PyTorch datasets by default support only __len__ and __getitem__ operations
+    """
+
+    def __init__(self, train: data.Dataset, validation: data.Dataset,
+                 test: typing.Optional[data.Dataset] = None, metadata: typing.Optional[dict] = None):
+        self.train = train
+        self.validation = validation
+        self.test = test
+
+        self.metadata = {} if metadata is None else metadata
+
+
+class LanguageSource(Source):
+    """ Special source for language datasets that handles dictionaries/encodings """
+
+    def __init__(self, train: data.Dataset, validation: data.Dataset,
+                 fields: dict, mapping: dict,
+                 test: typing.Optional[data.Dataset] = None, metadata: typing.Optional[dict] = None):
+        super().__init__(train, validation, test, metadata)
+
+        self.fields = fields
+        self.mapping = mapping
+
+
+# class SupervisedTextData(Source):
+#     """ An NLP torchtext data source """
+#     def __init__(self, train_source, val_source, train_iterator, val_iterator, data_field, target_field):
+#         super().__init__()
+#
+#         self.train_source = train_source
+#         self.val_source = val_source
+#         self.train_iterator = train_iterator
+#         self.val_iterator = val_iterator
+#         self.data_field = data_field
+#         self.target_field = target_field
+#
+#     @property
+#     def train_loader(self):
+#         """ PyTorch loader of training data """
+#         return self.train_iterator
+#
+#     @property
+#     def val_loader(self):
+#         """ PyTorch loader of validation data """
+#         return self.val_iterator
+#
+#     @property
+#     def train_dataset(self):
+#         """ Return the training dataset """
+#         return self.train_source
+#
+#     @property
+#     def val_dataset(self):
+#         """ Return the validation dataset """
+#         return self.val_source
+#
+#     @property
+#     def train_iterations_per_epoch(self):
+#         """ Return number of iterations per epoch """
+#         return len(self.train_iterator)
+#
+#     @property
+#     def val_iterations_per_epoch(self):
+#         """ Return number of iterations per epoch - validation """
+#         return len(self.val_iterator)
diff --git a/vel/api/transformation.py b/vel/api/transformation.py
new file mode 100644
index 00000000..42f1989c
--- /dev/null
+++ b/vel/api/transformation.py
@@ -0,0 +1,65 @@
+class Transformation:
+    """ Base class for all data augmentations """
+    def __init__(self, tags=None):
+        self.tags = ['train', 'val', 'test'] if tags is None else tags
+
+        if isinstance(self.tags, str):
+            self.tags = [self.tags]
+
+    def initialize(self, source):
+        """ Initialize transformation from source """
+        pass
+
+    def __call__(self, datapoint):
+        """ Do the transformation """
+        raise NotImplementedError
+
+    def denormalize(self, datapoint):
+        """ Operation reverse to normalization """
+        return datapoint
+
+    def denormalize_item(self, datapoint_item, coordinate):
+        """ Denormalize only a single item of the datapoint """
+        return datapoint_item
+
+
+class ScopedTransformation(Transformation):
+    """ Transformation applied only to certain keys of the datapoint """
+
+    def __init__(self, scope=None, tags=None):
+        super().__init__(tags)
+
+        self.scope = ['x'] if scope is None else scope
+
+        # If there is only one, we wrap it as a list
+        if isinstance(self.scope, str):
+            self.scope = [self.scope]
+
+    def transform(self, value):
+        """ Actual transformation code """
+        raise NotImplementedError
+
+    def denormalization_transform(self, value):
+        """ Operation reverse to normalization """
+        return value
+
+    def __call__(self, datapoint):
+        """ Do the transformation """
+        for name in self.scope:
+            datapoint[name] = self.transform(datapoint[name])
+
+        return datapoint
+
+    def denormalize(self, datapoint):
+        """ Operation reverse to normalization """
+        for name in self.scope:
+            datapoint[name] = self.denormalization_transform(datapoint[name])
+
+        return datapoint
+
+    def denormalize_item(self, datapoint_item, coordinate):
+        """ Denormalize only a single item of the datapoint """
+        if coordinate in self.scope:
+            return self.denormalization_transform(datapoint_item)
+        else:
+            return datapoint_item
diff --git a/vel/api/vmodule.py b/vel/api/vmodule.py
new file mode 100644
index 00000000..4c6d9c50
--- /dev/null
+++ b/vel/api/vmodule.py
@@ -0,0 +1,32 @@
+import torch.nn as nn
+
+from .size_hint import SizeHints
+
+
+class VModule(nn.Module):
+    """ Vel wrapper over nn.Module offering a few internally useful utilities """
+
+    def reset_weights(self):
+        """ Call proper initializers for the weights """
+        pass
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return False
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        return None
+
+    def grouped_parameters(self):
+        """ Return iterable of parameters (group, parameters) """
+        return [("default", self.parameters())]
+
+
+class BackboneModule(VModule):
+    """ Network, whose output feeds into other models. Needs to provide size hints. """
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        raise NotImplementedError
diff --git a/vel/augmentations/normalize.py b/vel/augmentations/normalize.py
deleted file mode 100644
index 2a1333d9..00000000
--- a/vel/augmentations/normalize.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import numpy as np
-
-import vel.api.data as data
-
-
-class Normalize(data.Augmentation):
-    """ Normalize input mean and standard deviation """
-
-    def __init__(self, mean, std, mode='x', tags=None):
-        super().__init__(mode, tags)
-        self.mean = np.array(mean, dtype=np.float32)
-        self.std = np.array(std, dtype=np.float32)
-
-    def __call__(self, x_data):
-        return (x_data - self.mean) / self.std
-
-    def denormalize(self, x_data):
-        """ Operation reverse to normalization """
-        return x_data * self.std + self.mean
-
-
-def create(mean, std, mode='x', tags=None):
-    """ Vel factory function """
-    return Normalize(mean=mean, std=std, mode=mode, tags=tags)
-
diff --git a/vel/augmentations/random_lighting.py b/vel/augmentations/random_lighting.py
deleted file mode 100644
index b5c0da5a..00000000
--- a/vel/augmentations/random_lighting.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import random
-
-import vel.api.data as data
-
-
-class RandomLighting(data.Augmentation):
-    """ Apply a horizontal flip randomly to input images """
-
-    def __init__(self, b, c, mode='x', tags=None):
-        super().__init__(mode, tags)
-        self.b, self.c = b, c
-
-    def __call__(self, img):
-        """ Adjust lighting """
-        rand_b = random.uniform(-self.b, self.b)
-        rand_c = random.uniform(-self.c, self.c)
-        rand_c = -1/(rand_c-1) if rand_c<0 else rand_c+1
-        return data.lighting(img, rand_b, rand_c)
-
-    def __repr__(self):
-        return self.__class__.__name__ + '(p={})'.format(self.p)
-
-
-def create(b, c, mode='x', tags=None):
-    return RandomLighting(b, c, mode, tags)
diff --git a/vel/augmentations/scale_min_size.py b/vel/augmentations/scale_min_size.py
deleted file mode 100644
index a89f0c8e..00000000
--- a/vel/augmentations/scale_min_size.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""
-Code based on:
-https://github.com/fastai/fastai/blob/master/fastai/transforms.py
-"""
-import PIL.Image as Image
-
-import vel.api.data as data
-
-
-class ScaleMinSize(data.Augmentation):
-    """ Scales the image so that the smallest axis is of 'size'. """
-    def __init__(self, size, mode='x', tags=None):
-        super().__init__(mode, tags)
-        self.size = size
-
-    def __call__(self, x_data):
-        return data.scale_min(x_data, self.size, Image.BILINEAR)
-
-
-def create(size, mode='x', tags=None):
-    """ Vel factory function """
-    return ScaleMinSize(size, mode, tags)
diff --git a/vel/augmentations/to_array.py b/vel/augmentations/to_array.py
deleted file mode 100644
index 3f8c980f..00000000
--- a/vel/augmentations/to_array.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import numpy as np
-
-import vel.api.data as data
-
-
-class ToArray(data.Augmentation):
-    """ Convert imate to an array of floats """
-    def __init__(self, mode='x', tags=None):
-        super().__init__(mode, tags)
-
-    def __call__(self, x_data):
-        array = np.array(x_data)
-
-        if array.dtype == np.uint8:
-            return array.astype(np.float32) / 255.0
-        else:
-            return array
-
-
-def create(mode='x', tags=None):
-    return ToArray(mode, tags)
diff --git a/vel/augmentations/to_tensor.py b/vel/augmentations/to_tensor.py
deleted file mode 100644
index dc4030e0..00000000
--- a/vel/augmentations/to_tensor.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-
-import torchvision.transforms.functional as F
-
-import vel.api.data as data
-
-
-class ToTensor(data.Augmentation):
-    """ Convert image array to a tensor """
-    def __init__(self, mode='x', tags=None):
-        super().__init__(mode, tags)
-
-    def __call__(self, datum):
-        if len(datum.shape) == 2:
-            # If the image has only one channel, it still needs to be specified
-            datum = datum.reshape(datum.shape[0], datum.shape[1], 1)
-
-        return F.to_tensor(datum)
-
-    def denormalize(self, datum):
-        return np.transpose(datum.numpy(), (1, 2, 0))
-
-
-def create(mode='x', tags=None):
-    """ Vel factory function """
-    return ToTensor(mode, tags)
diff --git a/vel/augmentations/tta/train_tta.py b/vel/augmentations/tta/train_tta.py
deleted file mode 100644
index 212937da..00000000
--- a/vel/augmentations/tta/train_tta.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# import torch
-# import torch.utils.data as tdata
-#
-# import vel.api.data as wdata
-#
-#
-# class TrainTTALoader:
-#     def __init__(self, n_augmentations, batch_size, data_source, augmentations, num_workers):
-#         self.n_augmentations = n_augmentations
-#         self.data_source = data_source
-#         self.augmentations = augmentations
-#
-#         self.val_ds = wdata.DataFlow(self.data_source, augmentations, tag='val')
-#         self.train_ds = wdata.DataFlow(self.data_source, augmentations, tag='train')
-#
-#         self.val_loader = tdata.DataLoader(
-#             self.val_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers
-#         )
-#
-#         self.train_loader = tdata.DataLoader(
-#             self.train_ds, batch_size=batch_size, shuffle=False, num_workers=num_workers
-#         )
-#
-#     def __len__(self):
-#         return (1 + self.n_augmentations) * len(self.val_loader)
-#
-#     def __iter__(self):
-#         iterlist = [iter(self.val_loader)]
-#
-#         for _ in range(self.n_augmentations):
-#             iterlist.append(iter(self.train_loader))
-#
-#         for _ in range(len(self.val_loader)):
-#             for iterator in iterlist:
-#                 yield next(iterator)
-#
-#
-# class TrainTTAAccumulator:
-#     def __init__(self, metric_accumulator, n_augmentations, data_source):
-#         self.metric_accumulator = metric_accumulator
-#
-#         self.source_elements = len(data_source)
-#         self.n_augmentations = n_augmentations
-#
-#         self.data = None
-#         self.target = None
-#
-#         self.accumulated_output = []
-#         self.accumulated_context = []
-#
-#         self.index = 0
-#
-#     # def calculate(self, data, target, output, context):
-#     def calculate(self, data_dict):
-#         """ Accumulate results """
-#         data = data_dict['data']
-#         target = data_dict['target']
-#         output = data_dict['output']
-#
-#         if self.index == 0:
-#             self.data = data
-#
-#         self.target = target
-#
-#         self.accumulated_output.append(output)
-#         self.accumulated_context.append(context)
-#
-#         self.index += 1
-#
-#         if self.index == (1 + self.n_augmentations):
-#             new_output = torch.mean(torch.stack(self.accumulated_output, dim=-1), dim=-1)
-#             new_context = {
-#                 k: torch.mean(torch.stack([c[k] for c in self.accumulated_context], dim=-1), dim=-1) for k in context.keys()
-#             }
-#
-#             self.metric_accumulator.calculate(self.data, self.target, new_output, new_context)
-#
-#             self.index = 0
-#             self.data = None
-#             self.target = None
-#             self.accumulated_output = []
-#             self.accumulated_context = []
-#
-#
-# class TrainTTA:
-#     """ Test time augmentation that generates additional samples according to the training set augmentations """
-#     def __init__(self, n_augmentations):
-#         self.n_augmentations = n_augmentations
-#
-#     def loader(self, data_source, augmentations, batch_size, num_workers):
-#         """ Return loader for the test-time-augmentation set """
-#         return TrainTTALoader(
-#             n_augmentations=self.n_augmentations,
-#             batch_size=batch_size,
-#             data_source=data_source,
-#             augmentations=augmentations,
-#             num_workers=num_workers
-#         )
-#
-#     def accumulator(self, metric_accumulator, val_source):
-#         """ Reset internal state """
-#         return TrainTTAAccumulator(metric_accumulator, self.n_augmentations, val_source)
-#
-#
-# def create(n_augmentations):
-#     return TrainTTA(n_augmentations)
diff --git a/vel/augmentations/__init__.py b/vel/callback/__init__.py
similarity index 100%
rename from vel/augmentations/__init__.py
rename to vel/callback/__init__.py
diff --git a/vel/callback/sample_tracker.py b/vel/callback/sample_tracker.py
new file mode 100644
index 00000000..aadefd12
--- /dev/null
+++ b/vel/callback/sample_tracker.py
@@ -0,0 +1,25 @@
+import typing
+import collections
+
+from vel.api import BatchInfo, TrainingInfo, Callback
+
+
+class SampleTracker(Callback):
+    """ Callback that calculates number of samples processed during the training process """
+
+    def on_initialization(self, training_info: TrainingInfo):
+        training_info['samples'] = collections.defaultdict(int)
+
+    def on_batch_end(self, batch_info: BatchInfo, dataset: typing.Optional[str] = None) -> None:
+        samples = batch_info['datapoint']['x'].shape[0]
+
+        batch_info['samples'] = samples
+
+        if dataset is not None:
+            batch_info.training_info['samples'][dataset] += samples
+
+    def write_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict):
+        hidden_state_dict['sample_tracker/samples'] = training_info['samples']
+
+    def load_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict):
+        training_info['samples'] = hidden_state_dict['sample_tracker/samples']
diff --git a/vel/callbacks/time_tracker.py b/vel/callback/time_tracker.py
similarity index 80%
rename from vel/callbacks/time_tracker.py
rename to vel/callback/time_tracker.py
index 8a59f9d1..6e298c50 100644
--- a/vel/callbacks/time_tracker.py
+++ b/vel/callback/time_tracker.py
@@ -1,20 +1,21 @@
 import time
 
-from vel.api import BatchInfo, TrainingInfo, Callback
+from vel.api import BatchInfo, TrainingInfo, Callback, Model
 
 
 class TimeTracker(Callback):
     """ Track training time - in seconds """
+
     def __init__(self):
         self.start_time = None
 
     def on_initialization(self, training_info: TrainingInfo):
         training_info['time'] = 0.0
 
-    def on_train_begin(self, training_info: TrainingInfo):
+    def on_train_begin(self, training_info: TrainingInfo, model: Model):
         self.start_time = time.time()
 
-    def on_batch_end(self, batch_info: BatchInfo):
+    def on_batch_end(self, batch_info: BatchInfo, dataset=None):
         current_time = time.time()
         batch_time = current_time - self.start_time
         self.start_time = current_time
diff --git a/vel/augmentations/tta/__init__.py b/vel/command/__init__.py
similarity index 100%
rename from vel/augmentations/tta/__init__.py
rename to vel/command/__init__.py
diff --git a/vel/commands/augvis_command.py b/vel/command/augvis_command.py
similarity index 52%
rename from vel/commands/augvis_command.py
rename to vel/command/augvis_command.py
index bd56ee6a..734f5e44 100644
--- a/vel/commands/augvis_command.py
+++ b/vel/command/augvis_command.py
@@ -1,19 +1,19 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from vel.api import Source
+from vel.data.loader import DatasetLoader
 
 
 class AugmentationVisualizationCommand:
     """ Visualize augmentations """
-    def __init__(self, source: Source, samples, cases):
-        self.source = source
+    def __init__(self, loader: DatasetLoader, samples, cases):
+        self.loader = loader
         self.samples = samples
         self.cases = cases
 
     def run(self):
         """ Run the visualization """
-        dataset = self.source.train_dataset()
+        dataset = self.loader.transformed_source.train
         num_samples = len(dataset)
 
         fig, ax = plt.subplots(self.cases, self.samples+1)
@@ -21,19 +21,19 @@ def run(self):
         selected_sample = np.sort(np.random.choice(num_samples, self.cases, replace=False))
 
         for i in range(self.cases):
-            raw_image, _ = dataset.get_raw(selected_sample[i])
+            raw_image = dataset.get_raw(selected_sample[i])['x']
 
             ax[i, 0].imshow(raw_image)
             ax[i, 0].set_title("Original image")
 
             for j in range(self.samples):
-                augmented_image, _ = dataset[selected_sample[i]]
-                augmented_image = dataset.denormalize(augmented_image)
-                ax[i, j+1].imshow(augmented_image)
+                augmented_datapoint = dataset[selected_sample[i]]
+                denormalized_datapoint = dataset.denormalize(augmented_datapoint)
+                ax[i, j+1].imshow(np.clip(denormalized_datapoint['x'], 0.0, 1.0))
 
         plt.show()
 
 
-def create(source, samples, cases):
+def create(loader: DatasetLoader, samples: int, cases: int):
     """ Vel factory function """
-    return AugmentationVisualizationCommand(source, samples, cases)
+    return AugmentationVisualizationCommand(loader, samples, cases)
diff --git a/vel/callbacks/__init__.py b/vel/command/latent/__init__.py
similarity index 100%
rename from vel/callbacks/__init__.py
rename to vel/command/latent/__init__.py
diff --git a/vel/command/latent/vae_nll.py b/vel/command/latent/vae_nll.py
new file mode 100644
index 00000000..9aafa3c0
--- /dev/null
+++ b/vel/command/latent/vae_nll.py
@@ -0,0 +1,53 @@
+import numpy as np
+import torch
+import tqdm
+
+from vel.api import TrainingInfo
+
+
+class VaeNllCommand:
+    """ Calculate NLL for the VAE using importance sampling """
+    def __init__(self, model_config, model_factory, loader, storage, max_batch: int, samples: int):
+        self.model_config = model_config
+        self.model_factory = model_factory
+        self.loader = loader
+        self.storage = storage
+
+        self.max_batch = max_batch
+        self.samples = samples
+
+    @torch.no_grad()
+    def run(self):
+        device = self.model_config.torch_device()
+        model = self.model_factory.instantiate().to(device)
+
+        start_epoch = self.storage.last_epoch_idx()
+
+        training_info = TrainingInfo(start_epoch_idx=start_epoch)
+
+        model_state, hidden_state = self.storage.load(training_info)
+        model.load_state_dict(model_state)
+
+        model.eval()
+
+        validation_dataset = self.loader.source.validation
+
+        results = []
+
+        # Always take at least one
+        batch_size = max(self.max_batch // self.samples, 1)
+
+        for i in tqdm.trange(validation_dataset.num_batches(batch_size)):
+            batch = validation_dataset.get_batch(i, batch_size)['x'].to(self.model_config.device)
+            nll = model.nll(batch, num_posterior_samples=self.samples)
+
+            results.append(nll.cpu().numpy())
+
+        full_results = np.concatenate(results)
+
+        print("NLL: {:.2f}".format(np.mean(full_results)))
+
+
+def create(model_config, model, loader, storage, max_batch: int = 1024, samples: int = 100):
+    """ Vel factory function """
+    return VaeNllCommand(model_config, model, loader, storage, max_batch=max_batch, samples=samples)
diff --git a/vel/command/list_command.py b/vel/command/list_command.py
new file mode 100644
index 00000000..11e37df8
--- /dev/null
+++ b/vel/command/list_command.py
@@ -0,0 +1,33 @@
+import os
+import os.path
+import glob
+import json
+
+from vel.api import ModelConfig
+
+
+class ListCommand:
+    """ List trained models for given config and their basic metadata """
+
+    def __init__(self, model_config: ModelConfig):
+        self.model_config = model_config
+
+    def run(self):
+        meta_dir = self.model_config.output_dir('meta', self.model_config.name)
+        meta_paths = os.path.join(meta_dir, '*', 'meta.json')
+
+        for path in sorted(glob.glob(meta_paths)):
+            with open(path, 'rt') as fp:
+                meta = json.load(fp)
+
+            print("-" * 80)
+            print("Run name: {}".format(meta['run_name']))
+            print("Tag: {}".format(meta['tag']))
+            print("Created: {}".format(meta['created']))
+
+
+def create(model_config):
+    """ Vel factory function """
+    return ListCommand(
+        model_config=model_config,
+    )
diff --git a/vel/commands/lr_find_command.py b/vel/command/lr_find_command.py
similarity index 88%
rename from vel/commands/lr_find_command.py
rename to vel/command/lr_find_command.py
index f3a67c47..ff927a52 100644
--- a/vel/commands/lr_find_command.py
+++ b/vel/command/lr_find_command.py
@@ -6,10 +6,11 @@
 import numpy as np
 import tqdm
 
-import vel.util.intepolate as interp
+import vel.util.interpolate as interp
 
-from vel.api import Learner, TrainingInfo, EpochInfo, BatchInfo
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
+from vel.api import TrainingInfo, EpochInfo, BatchInfo
+from vel.metric.base.averaging_metric import AveragingNamedMetric
+from vel.train import Trainer
 
 
 class LrFindCommand:
@@ -45,11 +46,11 @@ class LrFindCommand:
         http://arxiv.org/abs/1506.01186
 
     """
-    def __init__(self, model_config, model, source, optimizer_factory, start_lr=1e-5, end_lr=10, num_it=100,
+    def __init__(self, model_config, model, loader, optimizer_factory, start_lr=1e-5, end_lr=10, num_it=100,
                  interpolation='logscale', freeze=False, stop_dv=True, divergence_threshold=4.0, metric='loss'):
         # Mandatory pieces
         self.model = model
-        self.source = source
+        self.loader = loader
         self.optimizer_factory = optimizer_factory
         self.model_config = model_config
         # Settings
@@ -65,7 +66,7 @@ def __init__(self, model_config, model, source, optimizer_factory, start_lr=1e-5
     def run(self):
         """ Run the command with supplied configuration """
         device = self.model_config.torch_device()
-        learner = Learner(device, self.model.instantiate())
+        learner = Trainer(device, self.model.instantiate())
 
         lr_schedule = interp.interpolate_series(self.start_lr, self.end_lr, self.num_it, self.interpolation)
 
@@ -75,7 +76,7 @@ def run(self):
         # Optimizer shoudl be created after freeze
         optimizer = self.optimizer_factory.instantiate(learner.model)
 
-        iterator = iter(self.source.train_loader())
+        iterator = iter(self.loader['train'])
 
         # Metrics to track through this training
         metrics = learner.metrics() + [AveragingNamedMetric("lr")]
@@ -99,12 +100,12 @@ def run(self):
                 param_group['lr'] = lr
 
             try:
-                data, target = next(iterator)
+                datapoint = next(iterator)
             except StopIteration:
-                iterator = iter(self.source.train_loader())
-                data, target = next(iterator)
+                iterator = iter(self.loader['train'])
+                datapoint = next(iterator)
 
-            learner.train_batch(batch_info, data, target)
+            learner.train_batch(batch_info, datapoint)
 
             batch_info['lr'] = lr
 
@@ -149,13 +150,13 @@ def run(self):
         plt.show()
 
 
-def create(model_config, model, source, optimizer, start_lr=1e-5, end_lr=10, iterations=100, freeze=False,
+def create(model_config, model, loader, optimizer, start_lr=1e-5, end_lr=10, iterations=100, freeze=False,
            interpolation='logscale', stop_dv=True, divergence_threshold=4.0, metric='loss'):
     """ Vel factory function """
     return LrFindCommand(
         model_config=model_config,
         model=model,
-        source=source,
+        loader=loader,
         optimizer_factory=optimizer,
         start_lr=start_lr,
         end_lr=end_lr,
diff --git a/vel/commands/phase_train_command.py b/vel/command/phase_train_command.py
similarity index 69%
rename from vel/commands/phase_train_command.py
rename to vel/command/phase_train_command.py
index fc541286..7992364a 100644
--- a/vel/commands/phase_train_command.py
+++ b/vel/command/phase_train_command.py
@@ -1,19 +1,25 @@
-import torch
 import numpy as np
 import bisect
 import typing
 
-from vel.api import Learner, TrainingInfo, ModelConfig, TrainPhase
+import vel.api as api
+import vel.train as train
+
+from vel.data.loader import DatasetLoader
+from vel.metric.samples_per_sec import SamplesPerSec
+from vel.callback.time_tracker import TimeTracker
+from vel.callback.sample_tracker import SampleTracker
 
 
 class PhaseTrainCommand:
     """ Training  command - learn according to a set of phases """
 
-    def __init__(self, model_config: ModelConfig, model_factory, source, storage, phases: typing.List[TrainPhase],
+    def __init__(self, model_config: api.ModelConfig, model_factory: api.ModuleFactory, loader: DatasetLoader,
+                 storage: api.Storage, phases: typing.List[train.TrainPhase],
                  callbacks=None, restart=True):
         self.model_config = model_config
         self.model_factory = model_factory
-        self.source = source
+        self.loader = loader
         self.storage = storage
         self.phases = phases
         self.ladder = self._build_phase_ladder(phases)
@@ -50,29 +56,23 @@ def _select_phase_right_bound(self, epoch_number):
     def run(self):
         """ Run the command with supplied configuration """
         device = self.model_config.torch_device()
-        learner = Learner(device, self.model_factory.instantiate())
-
-        # All callbacks useful for learning
-        callbacks = self.gather_callbacks()
-
-        # Metrics to track through this training
-        metrics = learner.metrics()
+        trainer = train.Trainer(device, self.model_factory.instantiate())
 
         # Check if training was already started and potentially continue where we left off
-        training_info, hidden_state = self.resume_training(learner, callbacks, metrics)
+        training_info, hidden_state = self.start_training(trainer)
 
         # Prepare current training phase
         current_phase_idx = self._select_phase_left_bound(training_info.start_epoch_idx)
         current_phase = self.phases[current_phase_idx]
         local_idx = training_info.start_epoch_idx - self.ladder[current_phase_idx]
 
-        current_phase.set_up_phase(training_info, learner.model, self.source)
+        current_phase.set_up_phase(training_info, trainer.model, self.loader)
         print(current_phase.banner())
 
         if training_info.start_epoch_idx > 0:
-            current_phase.restore(training_info, local_idx, learner.model, hidden_state)
+            current_phase.restore(training_info, local_idx, trainer.model, hidden_state)
 
-        training_info.on_train_begin()
+        training_info.on_train_begin(trainer.model)
 
         for global_epoch_idx in range(training_info.start_epoch_idx + 1, self.full_number_of_epochs + 1):
             iteration_phase_idx = self._select_phase_right_bound(global_epoch_idx-1)
@@ -80,72 +80,74 @@ def run(self):
 
             # Phase preparations
             while current_phase_idx != iteration_phase_idx:
-                current_phase.tear_down_phase(training_info, learner.model)
+                current_phase.tear_down_phase(training_info, trainer.model)
 
                 current_phase_idx += 1
                 current_phase = self.phases[current_phase_idx]
 
-                current_phase.set_up_phase(training_info, learner.model, self.source)
+                current_phase.set_up_phase(training_info, trainer.model, self.loader)
                 print(current_phase.banner())
 
             # Create epoch info
             epoch_info = current_phase.epoch_info(training_info, global_epoch_idx, local_idx)
 
             # Execute learning
-            current_phase.execute_epoch(epoch_info, learner)
+            current_phase.execute_epoch(epoch_info, trainer)
 
             # Epoch checkpoint
-            self.storage.checkpoint(epoch_info, learner.model)
+            self.storage.checkpoint(epoch_info, trainer.model)
 
         # Tear down the last phase
         if current_phase is not None:
-            current_phase.tear_down_phase(training_info, learner.model)
+            current_phase.tear_down_phase(training_info, trainer.model)
 
         training_info.on_train_end()
 
         return training_info
 
-    def gather_callbacks(self) -> list:
-        """ Gather all the callbacks to be used in this training run """
-        callbacks = []
-
-        callbacks.extend(self.callbacks)
-        callbacks.extend(self.storage.streaming_callbacks())
-
-        return callbacks
-
-    def resume_training(self, learner, callbacks, metrics) -> (TrainingInfo, dict):
+    def start_training(self, trainer) -> (api.TrainingInfo, dict):
         """ Possibly resume training from a saved state from the storage """
-        if self.model_config.continue_training:
+        if self.model_config.resume_training:
             start_epoch = self.storage.last_epoch_idx()
         else:
             start_epoch = 0
 
-        training_info = TrainingInfo(
+        # Initial set of callbacks, always useful
+        callbacks = [TimeTracker(), SampleTracker()]
+
+        callbacks.extend(self.callbacks)
+        callbacks.extend(self.storage.streaming_callbacks())
+
+        # Metrics to track through this training
+        metrics = trainer.metrics() + [SamplesPerSec()]
+
+        training_info = api.TrainingInfo(
             start_epoch_idx=start_epoch,
-            run_name=self.model_config.run_name,
             metrics=metrics,
             callbacks=callbacks
         )
 
         if start_epoch == 0:
+            self.model_config.write_meta()
             self.storage.reset(self.model_config.render_configuration())
             training_info.initialize()
-            learner.initialize_training(training_info)
+            trainer.initialize_training(training_info)
             hidden_state = None
         else:
             model_state, hidden_state = self.storage.load(training_info)
-            learner.initialize_training(training_info, model_state, hidden_state)
+            training_info.restore(hidden_state)
+
+            trainer.initialize_training(training_info, model_state, hidden_state)
 
         return training_info, hidden_state
 
 
-def create(model_config, model, source, storage, phases, callbacks=None, restart=True):
+def create(model_config, model, loader, storage, phases, callbacks=None, restart=True):
     """ Vel factory function """
     return PhaseTrainCommand(
         model_config=model_config,
         model_factory=model,
-        source=source,
+        loader=loader,
         storage=storage,
         phases=phases,
         callbacks=callbacks,
diff --git a/vel/commands/__init__.py b/vel/command/rnn/__init__.py
similarity index 100%
rename from vel/commands/__init__.py
rename to vel/command/rnn/__init__.py
diff --git a/vel/commands/rnn/generate_text.py b/vel/command/rnn/generate_text.py
similarity index 91%
rename from vel/commands/rnn/generate_text.py
rename to vel/command/rnn/generate_text.py
index 7dda3b53..bf14f074 100644
--- a/vel/commands/rnn/generate_text.py
+++ b/vel/command/rnn/generate_text.py
@@ -6,6 +6,7 @@
 import torch.distributions as dist
 
 from vel.api import TrainingInfo
+from vel.util.tensor_util import to_device
 
 
 class GenerateTextCommand:
@@ -28,8 +29,7 @@ def run(self):
         start_epoch = self.storage.last_epoch_idx()
 
         training_info = TrainingInfo(
-            start_epoch_idx=start_epoch,
-            run_name=self.model_config.run_name,
+            start_epoch_idx=start_epoch
         )
 
         model_state, hidden_state = self.storage.load(training_info)
@@ -42,12 +42,12 @@ def run(self):
 
         generated_text = [current_char]
 
-        state = model.zero_state(1).to(device)
+        state = to_device(model.zero_state(1), device)
 
         char_tensor = torch.from_numpy(np.array([current_char_encoded])).view(1, 1).to(device)
 
         for _ in tqdm.trange(self.length):
-            prob_logits, state = model.forward_state(char_tensor, state)
+            prob_logits, state = model(char_tensor, state)
 
             # Apply temperature to the logits
             prob_logits = F.log_softmax(prob_logits.view(-1).div(self.temperature), dim=0)
diff --git a/vel/command/summary_command.py b/vel/command/summary_command.py
new file mode 100644
index 00000000..51ab11f7
--- /dev/null
+++ b/vel/command/summary_command.py
@@ -0,0 +1,14 @@
+class ModelSummary:
+    """ Just print model summary """
+    def __init__(self, model):
+        self.model_factory = model
+
+    def run(self, *args):
+        """ Print model summary """
+        model = self.model_factory.instantiate()
+        model.summary()
+
+
+def create(model):
+    """ Vel factory function """
+    return ModelSummary(model)
diff --git a/vel/commands/train_command.py b/vel/command/train_command.py
similarity index 56%
rename from vel/commands/train_command.py
rename to vel/command/train_command.py
index 7c798263..3ea825e6 100644
--- a/vel/commands/train_command.py
+++ b/vel/command/train_command.py
@@ -1,17 +1,21 @@
 import typing
 
 import vel.api as api
+import vel.train as train
 
-from vel.callbacks.time_tracker import TimeTracker
+from vel.data.loader import DatasetLoader
+from vel.metric.samples_per_sec import SamplesPerSec
+from vel.callback.time_tracker import TimeTracker
+from vel.callback.sample_tracker import SampleTracker
 
 
 class SimpleTrainCommand:
     """ Very simple training command - just run the supplied generators """
 
-    def __init__(self, epochs: int, model_config: api.ModelConfig, model_factory: api.ModelFactory,
+    def __init__(self, epochs: int, model_config: api.ModelConfig, model_factory: api.ModuleFactory,
                  optimizer_factory: api.OptimizerFactory, scheduler_factory: typing.Optional[api.SchedulerFactory],
-                 source: api.Source, storage: api.Storage, callbacks: typing.Optional[typing.List[api.Callback]],
-                 max_grad_norm: typing.Optional[float]):
+                 loader: DatasetLoader, storage: api.Storage,
+                 callbacks: typing.Optional[typing.List[api.Callback]]):
         self.epochs = epochs
         self.model_config = model_config
         self.model_factory = model_factory
@@ -19,87 +23,84 @@ def __init__(self, epochs: int, model_config: api.ModelConfig, model_factory: ap
         self.optimizer_factory = optimizer_factory
         self.scheduler_factory = scheduler_factory
 
-        self.source = source
+        self.loader = loader
         self.storage = storage
         self.callbacks = callbacks if callbacks is not None else []
-        self.max_grad_norm = max_grad_norm
 
     def run(self):
         """ Run the command with supplied configuration """
         device = self.model_config.torch_device()
 
-        learner = api.Learner(device, self.model_factory.instantiate(), self.max_grad_norm)
-        optimizer = self.optimizer_factory.instantiate(learner.model)
-
-        # All callbacks used for learning
-        callbacks = self.gather_callbacks(optimizer)
-
-        # Metrics to track through this training
-        metrics = learner.metrics()
+        trainer = train.Trainer(device, self.model_factory.instantiate())
+        optimizer = trainer.model.create_optimizer(self.optimizer_factory)
 
         # Check if training was already started and potentially continue where we left off
-        training_info = self.resume_training(learner, callbacks, metrics)
+        training_info = self.start_training(trainer, optimizer)
 
-        training_info.on_train_begin()
-
-        if training_info.optimizer_initial_state:
-            optimizer.load_state_dict(training_info.optimizer_initial_state)
+        training_info.on_train_begin(trainer.model)
 
         for global_epoch_idx in range(training_info.start_epoch_idx + 1, self.epochs + 1):
             epoch_info = api.EpochInfo(
                 training_info=training_info,
                 global_epoch_idx=global_epoch_idx,
-                batches_per_epoch=self.source.train_iterations_per_epoch(),
+                batches_per_epoch=self.loader.size['train'],
                 optimizer=optimizer
             )
 
             # Execute learning
-            learner.run_epoch(epoch_info, self.source)
+            trainer.run_epoch(epoch_info, self.loader)
 
-            self.storage.checkpoint(epoch_info, learner.model)
+            self.storage.checkpoint(epoch_info, trainer.model)
 
         training_info.on_train_end()
 
         return training_info
 
-    def gather_callbacks(self, optimizer) -> list:
-        """ Gather all the callbacks to be used in this training run """
-        callbacks = [TimeTracker()]
+    def start_training(self, trainer: train.Trainer, optimizer: api.VelOptimizer) -> api.TrainingInfo:
+        """ Possibly resume training from a saved state from the storage """
+        if self.model_config.resume_training:
+            start_epoch = self.storage.last_epoch_idx()
+        else:
+            start_epoch = 0
+
+        # Initial set of callbacks, always useful
+        callbacks = [TimeTracker(), SampleTracker()]
 
         if self.scheduler_factory is not None:
-            callbacks.append(self.scheduler_factory.instantiate(optimizer))
+            callbacks.extend(
+                optimizer.create_scheduler(scheduler_factory=self.scheduler_factory, last_epoch=start_epoch-1)
+            )
 
         callbacks.extend(self.callbacks)
         callbacks.extend(self.storage.streaming_callbacks())
 
-        return callbacks
-
-    def resume_training(self, learner, callbacks, metrics) -> api.TrainingInfo:
-        """ Possibly resume training from a saved state from the storage """
-        if self.model_config.continue_training:
-            start_epoch = self.storage.last_epoch_idx()
-        else:
-            start_epoch = 0
+        # Metrics to track through this training
+        metrics = trainer.metrics() + optimizer.metrics() + [SamplesPerSec()]
 
         training_info = api.TrainingInfo(
             start_epoch_idx=start_epoch,
-            run_name=self.model_config.run_name,
             metrics=metrics,
             callbacks=callbacks
         )
 
         if start_epoch == 0:
+            self.model_config.write_meta()
             self.storage.reset(self.model_config.render_configuration())
             training_info.initialize()
-            learner.initialize_training(training_info)
+            trainer.initialize_training(training_info)
         else:
             model_state, hidden_state = self.storage.load(training_info)
-            learner.initialize_training(training_info, model_state, hidden_state)
+
+            training_info.restore(hidden_state)
+            trainer.initialize_training(training_info, model_state, hidden_state)
+
+            if 'optimizer' in hidden_state:
+                optimizer.load_state_dict(hidden_state['optimizer'])
 
         return training_info
 
 
-def create(model_config, epochs, optimizer, model, source, storage, scheduler=None, callbacks=None, max_grad_norm=None):
+def create(model_config, epochs, optimizer, model, loader, storage, scheduler=None, callbacks=None):
     """ Vel factory function """
     return SimpleTrainCommand(
         epochs=epochs,
@@ -107,8 +108,7 @@ def create(model_config, epochs, optimizer, model, source, storage, scheduler=No
         model_factory=model,
         optimizer_factory=optimizer,
         scheduler_factory=scheduler,
-        source=source,
+        loader=loader,
         storage=storage,
         callbacks=callbacks,
-        max_grad_norm=max_grad_norm
     )
diff --git a/vel/commands/vis_store_command.py b/vel/command/vis_store_command.py
similarity index 100%
rename from vel/commands/vis_store_command.py
rename to vel/command/vis_store_command.py
diff --git a/vel/commands/summary_command.py b/vel/commands/summary_command.py
deleted file mode 100644
index 8da53b8a..00000000
--- a/vel/commands/summary_command.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from vel.api import Source
-
-
-class ModelSummary:
-    """ Just print model summary """
-    def __init__(self, model, source: Source):
-        self.model = model
-        self.source = source
-
-    def run(self, *args):
-        """ Print model summary """
-        if self.source is None:
-            self.model.summary()
-        else:
-            x_data, y_data = next(iter(self.source.train_loader()))
-            self.model.summary(input_size=x_data.shape[1:])
-
-
-def create(model, source=None):
-    """ Vel factory function """
-    return ModelSummary(model, source)
diff --git a/vel/data/__init__.py b/vel/data/__init__.py
new file mode 100644
index 00000000..3a6245c7
--- /dev/null
+++ b/vel/data/__init__.py
@@ -0,0 +1 @@
+from .dataflow import DataFlow
diff --git a/vel/commands/rnn/__init__.py b/vel/data/augmentation/__init__.py
similarity index 100%
rename from vel/commands/rnn/__init__.py
rename to vel/data/augmentation/__init__.py
diff --git a/vel/augmentations/center_crop.py b/vel/data/augmentation/center_crop.py
similarity index 52%
rename from vel/augmentations/center_crop.py
rename to vel/data/augmentation/center_crop.py
index b586c61b..8bead415 100644
--- a/vel/augmentations/center_crop.py
+++ b/vel/data/augmentation/center_crop.py
@@ -3,10 +3,11 @@
 https://github.com/fastai/fastai/blob/master/fastai/transforms.py
 """
 
-import vel.api.data as data
+import vel.api as api
+import vel.data.operation.image_op as op
 
 
-class CenterCrop(data.Augmentation):
+class CenterCrop(api.ScopedTransformation):
     """ A class that represents a Center Crop.
 
     This transforms (optionally) transforms x,y at with the same parameters.
@@ -17,14 +18,14 @@ class CenterCrop(data.Augmentation):
         tfm_y : TfmType
             type of y transformation.
     """
-    def __init__(self, size, mode='x', tags=None):
-        super().__init__(mode, tags)
+    def __init__(self, size, scope='x', tags=None):
+        super().__init__(scope, tags)
 
         self.size = size
 
-    def __call__(self, x):
-        return data.center_crop(x, self.size)
+    def transform(self, x):
+        return op.center_crop(x, self.size)
 
 
-def create(size, mode='x', tags=None):
-    return CenterCrop(size, mode, tags)
+def create(size, scope='x', tags=None):
+    return CenterCrop(size, scope, tags)
diff --git a/vel/augmentations/random_crop.py b/vel/data/augmentation/random_crop.py
similarity index 75%
rename from vel/augmentations/random_crop.py
rename to vel/data/augmentation/random_crop.py
index 3cb8faf5..ffdf89c1 100644
--- a/vel/augmentations/random_crop.py
+++ b/vel/data/augmentation/random_crop.py
@@ -6,10 +6,11 @@
 import numbers
 import random
 
-import vel.api.data as data
+import vel.api as api
+import vel.data.operation.image_op as image_op
 
 
-class RandomCrop(data.Augmentation):
+class RandomCrop(api.ScopedTransformation):
     """Crop the given PIL Image at a random location.
 
     Args:
@@ -24,8 +25,8 @@ class RandomCrop(data.Augmentation):
             desired size to avoid raising an exception.
     """
 
-    def __init__(self, size, padding=0, padding_mode='constant', pad_if_needed=False, mode='x', tags=None):
-        super().__init__(mode, tags)
+    def __init__(self, size, padding=0, padding_mode='constant', pad_if_needed=False, scope='x', tags=None):
+        super().__init__(scope, tags)
 
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
@@ -34,7 +35,7 @@ def __init__(self, size, padding=0, padding_mode='constant', pad_if_needed=False
 
         self.padding = padding
         self.padding_mode = padding_mode
-        self.padding_mode_cv = data.mode_to_cv2(self.padding_mode)
+        self.padding_mode_cv = image_op.mode_to_cv2(self.padding_mode)
         self.pad_if_needed = pad_if_needed
 
     @staticmethod
@@ -58,7 +59,7 @@ def get_params(img, output_size):
         j = random.randint(0, w - tw)
         return i, j, th, tw
 
-    def __call__(self, img):
+    def transform(self, img):
         """
         Args:
             img (PIL Image): Image to be cropped.
@@ -67,24 +68,24 @@ def __call__(self, img):
             PIL Image: Cropped image.
         """
         if self.padding > 0:
-            img = data.pad(img, self.padding, mode=self.padding_mode_cv)
+            img = image_op.pad(img, self.padding, mode=self.padding_mode_cv)
 
         # pad the width if needed
         if self.pad_if_needed and img.size[0] < self.size[1]:
-            img = data.pad(img, (int((1 + self.size[1] - img.size[0]) / 2), 0), mode=self.padding_mode_cv)
+            img = image_op.pad(img, (int((1 + self.size[1] - img.size[0]) / 2), 0), mode=self.padding_mode_cv)
 
         # pad the height if needed
         if self.pad_if_needed and img.size[1] < self.size[0]:
-            img = data.pad(img, (0, int((1 + self.size[0] - img.size[1]) / 2)), mode=self.padding_mode_cv)
+            img = image_op.pad(img, (0, int((1 + self.size[0] - img.size[1]) / 2)), mode=self.padding_mode_cv)
 
         i, j, h, w = self.get_params(img, self.size)
 
-        return data.crop(img, j, i, w, h)
+        return image_op.crop(img, j, i, w, h)
 
     def __repr__(self):
         return self.__class__.__name__ + '(size={0}, padding={1})'.format(self.size, self.padding)
 
 
-def create(width, height, padding=0, padding_mode='constant', mode='x', tags=None):
+def create(width, height, padding=0, padding_mode='constant', scope='x', tags=None):
     """ Vel factory function """
-    return RandomCrop(size=(width, height), padding=padding, padding_mode=padding_mode, mode=mode, tags=tags)
+    return RandomCrop(size=(width, height), padding=padding, padding_mode=padding_mode, scope=scope, tags=tags)
diff --git a/vel/augmentations/random_horizontal_flip.py b/vel/data/augmentation/random_horizontal_flip.py
similarity index 60%
rename from vel/augmentations/random_horizontal_flip.py
rename to vel/data/augmentation/random_horizontal_flip.py
index a5549673..b50caa8f 100644
--- a/vel/augmentations/random_horizontal_flip.py
+++ b/vel/data/augmentation/random_horizontal_flip.py
@@ -1,17 +1,17 @@
 import random
 import numpy as np
 
-import vel.api.data as data
+import vel.api as api
 
 
-class RandomHorizontalFlip(data.Augmentation):
+class RandomHorizontalFlip(api.ScopedTransformation):
     """ Apply a horizontal flip randomly to input images """
 
-    def __init__(self, p=0.5, mode='x', tags=None):
-        super().__init__(mode, tags)
+    def __init__(self, p=0.5, scope='x', tags=None):
+        super().__init__(scope, tags)
         self.p = p
 
-    def __call__(self, img):
+    def transform(self, img):
         """
         Args:
             img (PIL Image): Image to be flipped.
@@ -27,5 +27,5 @@ def __repr__(self):
         return self.__class__.__name__ + '(p={})'.format(self.p)
 
 
-def create(p=0.5):
-    return RandomHorizontalFlip(p)
\ No newline at end of file
+def create(p=0.5, scope='x', tags=None):
+    return RandomHorizontalFlip(p, scope=scope, tags=tags)
diff --git a/vel/data/augmentation/random_lighting.py b/vel/data/augmentation/random_lighting.py
new file mode 100644
index 00000000..8331d75e
--- /dev/null
+++ b/vel/data/augmentation/random_lighting.py
@@ -0,0 +1,26 @@
+import random
+
+import vel.api as api
+import vel.data.operation.image_op as op
+
+
+class RandomLighting(api.ScopedTransformation):
+    """ Apply a horizontal flip randomly to input images """
+
+    def __init__(self, b, c, mode='x', tags=None):
+        super().__init__(mode, tags)
+        self.b, self.c = b, c
+
+    def transform(self, img):
+        """ Adjust lighting """
+        rand_b = random.uniform(-self.b, self.b)
+        rand_c = random.uniform(-self.c, self.c)
+        rand_c = -1/(rand_c-1) if rand_c < 0 else rand_c+1
+        return op.lighting(img, rand_b, rand_c)
+
+    def __repr__(self):
+        return self.__class__.__name__ + '(b={}, c={})'.format(self.b, self.c)
+
+
+def create(b, c, scope='x', tags=None):
+    return RandomLighting(b, c, scope, tags)
diff --git a/vel/augmentations/random_rotate.py b/vel/data/augmentation/random_rotate.py
similarity index 51%
rename from vel/augmentations/random_rotate.py
rename to vel/data/augmentation/random_rotate.py
index 1a646b22..65ae8a04 100644
--- a/vel/augmentations/random_rotate.py
+++ b/vel/data/augmentation/random_rotate.py
@@ -5,25 +5,26 @@
 import cv2
 import random
 
-import vel.api.data as data
+import vel.api as api
+import vel.data.operation.image_op as op
 
 
-class RandomRotate(data.Augmentation):
+class RandomRotate(api.ScopedTransformation):
     """ Rotate image randomly by an angle between (-deg, +deg) """
-    def __init__(self, deg, p=0.75, mode='x', tags=None):
-        super().__init__(mode, tags)
+    def __init__(self, deg, p=0.75, scope='x', tags=None):
+        super().__init__(scope, tags)
         self.deg = deg
         self.p = p
 
-    def __call__(self, x_data):
+    def transform(self, x_data):
         if random.random() < self.p:
             random_degree = random.uniform(-self.deg, self.deg)
-            return data.rotate_img(x_data, random_degree, mode=cv2.BORDER_REFLECT)
+            return op.rotate_img(x_data, random_degree, mode=cv2.BORDER_REFLECT)
         else:
             # No, don't do it
             return x_data
 
 
-def create(deg, p=0.75, mode='x', tags=None):
+def create(deg, p=0.75, scope='x', tags=None):
     """ Vel factory function """
-    return RandomRotate(deg, p, mode, tags)
+    return RandomRotate(deg, p, scope, tags)
diff --git a/vel/augmentations/random_scale.py b/vel/data/augmentation/random_scale.py
similarity index 59%
rename from vel/augmentations/random_scale.py
rename to vel/data/augmentation/random_scale.py
index c60d5852..694cdf2f 100644
--- a/vel/augmentations/random_scale.py
+++ b/vel/data/augmentation/random_scale.py
@@ -6,18 +6,19 @@
 import collections.abc as abc
 import random
 
-import vel.api.data as data
+import vel.api as api
+import vel.data.operation.image_op as op
 
 
-class RandomScale(data.Augmentation):
+class RandomScale(api.ScopedTransformation):
     """ Scales the image so that the smallest axis is of 'size' times a random number between 1.0 and max_zoom. """
-    def __init__(self, size, max_zoom, p=0.75, mode='x', tags=None):
-        super().__init__(mode, tags)
+    def __init__(self, size, max_zoom, p=0.75, scope='x', tags=None):
+        super().__init__(scope, tags)
         self.size = size
         self.max_zoom = max_zoom
         self.p = p
 
-    def __call__(self, x_data):
+    def transform(self, x_data):
         if random.random() < self.p:
             # Yes, do it
             min_z = 1.
@@ -30,8 +31,9 @@ def __call__(self, x_data):
             # No, don't do it
             mult = 1.0
 
-        return data.scale_min(x_data, int(self.size * mult), cv2.INTER_AREA)
+        return op.scale_min(x_data, int(self.size * mult), cv2.INTER_AREA)
 
 
-def create(size, max_zoom, p=0.75, mode='x', tags=None):
-    return RandomScale(size, max_zoom, p, mode, tags)
+def create(size, max_zoom, p=0.75, scope='x', tags=None):
+    """ Vel factory function """
+    return RandomScale(size, max_zoom, p, scope, tags)
diff --git a/vel/data/augmentation/scale_min_size.py b/vel/data/augmentation/scale_min_size.py
new file mode 100644
index 00000000..0fae3bd6
--- /dev/null
+++ b/vel/data/augmentation/scale_min_size.py
@@ -0,0 +1,21 @@
+"""
+Code based on:
+https://github.com/fastai/fastai/blob/master/fastai/transforms.py
+"""
+import vel.api as api
+import vel.data.operation.image_op as op
+
+
+class ScaleMinSize(api.ScopedTransformation):
+    """ Scales the image so that the smallest axis is of 'size'. """
+    def __init__(self, size, scope='x', tags=None):
+        super().__init__(scope, tags)
+        self.size = size
+
+    def transform(self, x_data):
+        return op.scale_min(x_data, self.size)
+
+
+def create(size, scope='x', tags=None):
+    """ Vel factory function """
+    return ScaleMinSize(size, scope, tags)
diff --git a/vel/data/dataflow.py b/vel/data/dataflow.py
new file mode 100644
index 00000000..de7fb4ab
--- /dev/null
+++ b/vel/data/dataflow.py
@@ -0,0 +1,100 @@
+import collections
+import torch
+import torch.utils.data as data
+import typing
+
+from vel.api import Source, Transformation
+
+
+def pre_map(datapoint):
+    """ Map datapoint from a list into the dictionary """
+    if isinstance(datapoint, (list, tuple)):
+        return dict(zip("xyzw", datapoint))
+
+    if 'x' in datapoint:
+        datapoint['size'] = datapoint['x'].shape[0]
+
+    return datapoint
+
+
+class DataFlow(data.Dataset):
+    """ A dataset wrapping underlying data source with transformations """
+
+    @staticmethod
+    def transform(source: Source, transformations: typing.List[Transformation]) -> Source:
+        """ Transform supplied source with a list of given transformations """
+        # Initialize transformations from source
+        for t in transformations:
+            t.initialize(source)
+
+        return Source(
+            train=DataFlow(source.train, transformations, 'train'),
+            validation=DataFlow(source.validation, transformations, 'val'),
+            test=None if source.test is None else DataFlow(source.test, transformations, 'test')
+        )
+
+    def __init__(self, dataset, transformations, tag):
+        self.dataset = dataset
+        self.tag = tag
+
+        if transformations is None:
+            self.transformations = []
+        else:
+            self.transformations = [t for t in transformations if tag in t.tags]
+
+    def get_raw(self, index):
+        """ Get raw data point """
+        return pre_map(self.dataset[index])
+
+    def get_batch(self, batch_idx, batch_size):
+        """
+        Simple method to get a batch of data, mainly for interactive purposes.
+        For training, a DataLoader should be used.
+        """
+
+        start_idx = batch_idx * batch_size
+        end_idx = min(start_idx + batch_size, len(self))
+
+        buffer = collections.defaultdict(list)
+
+        for i in range(start_idx, end_idx):
+            datapoint = self[i]
+
+            for k, v in datapoint.items():
+                buffer[k].append(v)
+
+        return {
+            k: torch.stack(v, dim=0) for k, v in buffer.items()
+        }
+
+    def num_batches(self, batch_size):
+        """ Number of batches of given batch size """
+        length = len(self)
+        return (length + (batch_size - 1)) // batch_size
+
+    def __getitem__(self, index):
+        """ Get data point from the dataset """
+        datapoint = self.get_raw(index)
+
+        for t in self.transformations:
+            datapoint = t(datapoint)
+
+        return datapoint
+
+    def denormalize(self, datapoint):
+        """ Perform a reverse normalization (for viewing) """
+        for t in self.transformations[::-1]:
+            datapoint = t.denormalize(datapoint)
+
+        return datapoint
+
+    def denormalize_item(self, datapoint_item, coordinate):
+        """ Perform a reverse normalization of a single item (for viewing) """
+        for t in self.transformations[::-1]:
+            datapoint_item = t.denormalize_item(datapoint_item, coordinate)
+
+        return datapoint_item
+
+    def __len__(self):
+        """ Length of the dataset """
+        return len(self.dataset)
diff --git a/vel/data/loader/__init__.py b/vel/data/loader/__init__.py
new file mode 100644
index 00000000..57122da0
--- /dev/null
+++ b/vel/data/loader/__init__.py
@@ -0,0 +1,3 @@
+from .dataset_loader import DatasetLoader
+from .bucket_loader import BucketLoader
+from .text_character_loader import TextCharacterLoader
diff --git a/vel/data/loader/bucket_loader.py b/vel/data/loader/bucket_loader.py
new file mode 100644
index 00000000..f587d4a6
--- /dev/null
+++ b/vel/data/loader/bucket_loader.py
@@ -0,0 +1,78 @@
+import torchtext.data as data
+
+from vel.util.dataloader import IteratorDictWrapper
+from vel.api import LanguageSource, ModelConfig
+
+
+class BucketLoader:
+    """ Loads sequence data from a source and batches together examples of similar length """
+
+    def __init__(self, model_config: ModelConfig, source: LanguageSource, batch_size: int):
+        self._source = source
+        self.batch_size = batch_size
+
+        if self._source.test is None:
+            self.train_loader, self.val_loader = data.BucketIterator.splits(
+                (self._source.train, self._source.validation),
+                batch_size=batch_size,
+                device=model_config.torch_device(),
+                shuffle=True
+            )
+            self.test_loader = None
+        else:
+            self.train_loader, self.val_loader, self.test_loader = data.BucketIterator.splits(
+                (self._source.train, self._source.validation, self._source.test),
+                batch_size=batch_size,
+                device=model_config.torch_device(),
+                shuffle=True
+            )
+
+        self.train_loader = IteratorDictWrapper(self.train_loader, self._source.mapping)
+        self.val_loader = IteratorDictWrapper(self.val_loader, self._source.mapping)
+
+        if self.test_loader:
+            self.test_loader = IteratorDictWrapper(self.test_loader, self._source.mapping)
+
+        self._loaders = {
+            'train': self.train_loader,
+            'val': self.val_loader,
+            'test': self.test_loader
+        }
+
+        self._loader_sizes = {
+            'train': len(self.train_loader),
+            'val': len(self.val_loader),
+            'test': 0 if self.test_loader is None else len(self.test_loader)
+        }
+
+    def __getitem__(self, item):
+        return self._loaders[item]
+
+    @property
+    def source(self):
+        """ Return the source for this loader """
+        return self._source
+
+    @property
+    def loader(self):
+        """ Get a dict of loaders """
+        return self._loaders
+
+    @property
+    def size(self):
+        """ Get a dict of sizes of each loader """
+        return self._loader_sizes
+
+    @property
+    def alphabet_size(self):
+        """ Size of the text alphabet """
+        return self._source.metadata.get('alphabet_size', 0)
+
+
+def create(model_config: ModelConfig, source: LanguageSource, batch_size: int):
+    """ Vel factory function """
+    return BucketLoader(
+        model_config=model_config,
+        source=source,
+        batch_size=batch_size,
+    )
diff --git a/vel/data/loader/dataset_loader.py b/vel/data/loader/dataset_loader.py
new file mode 100644
index 00000000..aa37fd62
--- /dev/null
+++ b/vel/data/loader/dataset_loader.py
@@ -0,0 +1,82 @@
+import typing
+import torch.utils.data as data
+
+from vel.api import Source
+
+from vel.data.dataflow import DataFlow
+
+
+class DatasetLoader:
+    """ Loads data from a data source to serve it to the model """
+
+    def __init__(self, source: Source, batch_size: int, num_workers: int,
+                 transformations: typing.Optional[list] = None, pin_memory=False):
+        self._source = source
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.transformations = transformations
+        self.pin_memory = pin_memory
+
+        if transformations is not None:
+            self.transformed_source = DataFlow.transform(self._source, transformations)
+        else:
+            self.transformed_source = source
+
+        self.train_loader = data.DataLoader(
+            self.transformed_source.train, batch_size=batch_size, shuffle=True, num_workers=num_workers,
+            pin_memory=pin_memory, drop_last=True
+        )
+
+        self.val_loader = data.DataLoader(
+            self.transformed_source.validation, batch_size=batch_size, shuffle=False, num_workers=num_workers,
+            pin_memory=pin_memory
+        )
+
+        if self.transformed_source.test is not None:
+            self.test_loader = data.DataLoader(
+                self.transformed_source.test, batch_size=batch_size, shuffle=False, num_workers=num_workers
+            )
+        else:
+            self.test_loader = None
+
+        self._loaders = {
+            'train': self.train_loader,
+            'val': self.val_loader,
+            'test': self.test_loader
+        }
+
+        self._loader_sizes = {
+            'train': len(self.train_loader),
+            'val': len(self.val_loader),
+            'test': 0 if self.test_loader is None else len(self.test_loader)
+        }
+
+    def __getitem__(self, item):
+        return self._loaders[item]
+
+    @property
+    def source(self):
+        """ Return the source for this loader """
+        return self.transformed_source
+
+    @property
+    def loader(self):
+        """ Get a dict of loaders """
+        return self._loaders
+
+    @property
+    def size(self):
+        """ Get a dict of sizes of each loader """
+        return self._loader_sizes
+
+
+def create(source: Source, batch_size: int, num_workers: int = 0, transformations: typing.Optional[list] = None,
+           pin_memory=False):
+    """ Vel factory function """
+    return DatasetLoader(
+        source=source,
+        batch_size=batch_size,
+        pin_memory=pin_memory,
+        num_workers=num_workers,
+        transformations=transformations
+    )
diff --git a/vel/data/loader/text_character_loader.py b/vel/data/loader/text_character_loader.py
new file mode 100644
index 00000000..d37e095f
--- /dev/null
+++ b/vel/data/loader/text_character_loader.py
@@ -0,0 +1,139 @@
+import numpy as np
+import torch
+
+from vel.api import Source
+
+
+class TextIterator:
+    """ Iterator over a text dataset """
+    def __init__(self, padded_sequence, sequence_length, batch_size, alphabet_size, num_batches):
+        self.sequence_length = sequence_length
+        self.batch_size = batch_size
+        self.num_batches = num_batches
+        self.alphabet_size = alphabet_size
+
+        self.padded_sequence = padded_sequence[:-1].reshape(self.num_batches * self.batch_size, self.sequence_length)
+        self.padded_sequence_next = padded_sequence[1:].reshape(
+            self.num_batches * self.batch_size, self.sequence_length
+        )
+
+        self.sequence_indices = np.arange(self.num_batches * self.batch_size)
+
+        np.random.shuffle(self.sequence_indices)
+
+        self.sequence_indices = self.sequence_indices.reshape(self.num_batches, self.batch_size)
+
+        self.batch_idx = 0
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.batch_idx == self.num_batches:
+            raise StopIteration
+        else:
+            input_data = torch.from_numpy(self.padded_sequence[self.sequence_indices[self.batch_idx]])
+            target_data = torch.from_numpy(self.padded_sequence_next[self.sequence_indices[self.batch_idx]])
+
+            self.batch_idx += 1
+
+            return {'x': input_data.to(torch.long), 'y': target_data.to(torch.long)}
+
+
+class TextLoader:
+    """ Creates iterators over a sequential block of text """
+    def __init__(self, sequence, sequence_length, batch_size, alphabet_size):
+        self.sequence = sequence
+        self.sequence_length = sequence_length
+        self.batch_size = batch_size
+        self.alphabet_size = alphabet_size
+
+        # 1 is for the last element as the target needs to be shifted by 1
+        residual_length = (len(self.sequence) - self.sequence_length - 1)
+        full_size = self.sequence_length * self.batch_size
+
+        rest = residual_length % full_size
+        self.num_batches = residual_length // full_size
+
+        if rest > 0:
+            self.sequence = np.pad(self.sequence, (0, full_size - rest), mode='constant')
+            self.num_batches += 1
+
+    def __iter__(self):
+        initial_offset = np.random.randint(self.sequence_length)
+        relevant_subsequence = self.sequence[
+            # 1 is for the last element as the target needs to be shifted by 1
+            initial_offset:self.num_batches * self.sequence_length * self.batch_size + initial_offset + 1
+        ]
+
+        return TextIterator(
+            relevant_subsequence, self.sequence_length, self.batch_size,
+            alphabet_size=self.alphabet_size,
+            num_batches=self.num_batches
+        )
+
+    def __len__(self):
+        """ Number of batches in this loader """
+        return self.num_batches
+
+
+class TextCharacterLoader:
+    """ Loader for the text character data source """
+
+    def __init__(self, source, sequence_length: int, batch_size: int):
+        self._source = source
+        self.sequence_length = sequence_length
+        self.batch_size = batch_size
+        self.alphabet = self._source.metadata['alphabet']
+
+        self.train_loader = TextLoader(self._source.train, self.sequence_length, self.batch_size, len(self.alphabet))
+        self.val_loader = TextLoader(self._source.validation, self.sequence_length, self.batch_size, len(self.alphabet))
+
+        if self._source.test is None:
+            self.test_loader = None
+        else:
+            self.test_loader = TextLoader(self._source.test, self.sequence_length, self.batch_size, len(self.alphabet))
+
+        self._loaders = {
+            'train': self.train_loader,
+            'val': self.val_loader,
+            'test': self.test_loader
+        }
+
+        self._loader_sizes = {
+            'train': len(self.train_loader),
+            'val': len(self.val_loader),
+            'test': 0 if self.test_loader is None else len(self.test_loader)
+        }
+
+    def __getitem__(self, item):
+        return self._loaders[item]
+
+    @property
+    def source(self):
+        """ Return source for this loader """
+        return self._source
+
+    @property
+    def alphabet_size(self):
+        """ Size of the text alphabet """
+        return len(self.alphabet)
+
+    @property
+    def loader(self):
+        """ Get a dict of loaders """
+        return self._loaders
+
+    @property
+    def size(self):
+        """ Get a dict of sizes of each loader """
+        return self._loader_sizes
+
+
+def create(source: Source, sequence_length: int = 64, batch_size: int = 64):
+    """ Vel factory function """
+    return TextCharacterLoader(
+        source=source,
+        sequence_length=sequence_length,
+        batch_size=batch_size
+    )
diff --git a/vel/internals/__init__.py b/vel/data/operation/__init__.py
similarity index 100%
rename from vel/internals/__init__.py
rename to vel/data/operation/__init__.py
diff --git a/vel/api/data/image_ops.py b/vel/data/operation/image_op.py
similarity index 61%
rename from vel/api/data/image_ops.py
rename to vel/data/operation/image_op.py
index 55c86d5e..9b6833f2 100644
--- a/vel/api/data/image_ops.py
+++ b/vel/data/operation/image_op.py
@@ -4,32 +4,29 @@
 
 
 def crop_square(im, r, c, sz):
-    '''
-    crop image into a square of size sz,
-    '''
-    return im[r:r+sz, c:c+sz]
+    """ Crop image into a square of size sz. """
+    return im[r:r + sz, c:c + sz]
 
 
 def crop(im, r, c, sz_h, sz_w):
-    '''
-    crop image into a square of size sz,
-    '''
-    return im[r:r+sz_h, c:c+sz_w]
+    """ Crop image into a of size sz_w x sz_h. """
+    return im[r:r + sz_h, c:c + sz_w]
 
 
 def center_crop(im, min_sz=None):
     """ Returns a center crop of an image"""
     # return F.center_crop(im, min_sz)
-    r,c,*_ = im.shape
-    if min_sz is None: min_sz = min(r,c)
-    start_r = math.ceil((r-min_sz)/2)
-    start_c = math.ceil((c-min_sz)/2)
+    r, c, *_ = im.shape
+    if min_sz is None:
+        min_sz = min(r, c)
+    start_r = math.ceil((r - min_sz) / 2)
+    start_c = math.ceil((c - min_sz) / 2)
     return crop_square(im, start_r, start_c, min_sz)
 
 
 def scale_to(x, ratio, targ):
-    '''Calculate dimension of an image during scaling with aspect ratio'''
-    return max(math.floor(x*ratio), targ)
+    """ Calculate dimension of an image during scaling with aspect ratio """
+    return max(math.floor(x * ratio), targ)
 
 
 def scale_min(im, targ, interpolation=cv2.INTER_AREA):
@@ -39,9 +36,9 @@ def scale_min(im, targ, interpolation=cv2.INTER_AREA):
         im (array): image
         targ (int): target size
     """
-    r,c,*_ = im.shape
+    r, c, *_ = im.shape
 
-    ratio = targ/min(r,c)
+    ratio = targ / min(r, c)
 
     sz = (scale_to(c, ratio, targ), scale_to(r, ratio, targ))
 
@@ -59,9 +56,12 @@ def rotate_img(im, deg, mode=cv2.BORDER_CONSTANT, interpolation=cv2.INTER_AREA):
     Arguments:
         deg (float): degree to rotate.
     """
-    r,c,*_ = im.shape
-    M = cv2.getRotationMatrix2D((c//2,r//2),deg,1)
-    return cv2.warpAffine(im,M,(c,r), borderMode=mode, flags=cv2.WARP_FILL_OUTLIERS+interpolation)
+    r, c, *_ = im.shape
+    M = cv2.getRotationMatrix2D((c // 2, r // 2), deg, 1)
+    return cv2.warpAffine(
+        im, M, (c, r), borderMode=mode,
+        flags=cv2.WARP_FILL_OUTLIERS + interpolation
+    )
 
 
 def pad(img, pad, mode=cv2.BORDER_REFLECT):
@@ -78,7 +78,8 @@ def mode_to_cv2(mode='constant'):
 
 
 def lighting(im, b, c):
-    ''' adjusts image's balance and contrast'''
-    if b==0 and c==1: return im
+    """ Adjusts image's balance and contrast. """
+    if b == 0 and c == 1:
+        return im
     mu = np.average(im)
-    return np.clip((im-mu)*c+mu+b,0.,1.).astype(np.float32)
+    return np.clip((im - mu) * c + mu + b, 0., 1.).astype(np.float32)
diff --git a/vel/internals/tests/__init__.py b/vel/data/source/__init__.py
similarity index 100%
rename from vel/internals/tests/__init__.py
rename to vel/data/source/__init__.py
diff --git a/vel/data/source/img_dir_source.py b/vel/data/source/img_dir_source.py
new file mode 100644
index 00000000..45b4ec2b
--- /dev/null
+++ b/vel/data/source/img_dir_source.py
@@ -0,0 +1,48 @@
+import os.path
+import zipfile
+
+import torchvision.datasets as ds
+import torchvision.datasets.utils as ds_util
+
+from vel.api import Source
+
+
+class ImageDirSource(ds.ImageFolder):
+    """ Source where images are grouped by class in folders """
+    pass
+
+
+def create(model_config, path, url=None, extract_parent=False):
+    """ Create an ImageDirSource with supplied arguments """
+    if not os.path.isabs(path):
+        path = model_config.project_top_dir(path)
+
+    train_path = os.path.join(path, 'train')
+    valid_path = os.path.join(path, 'valid')
+
+    if not os.path.exists(train_path) or not os.path.exists(valid_path):
+        filename = url.rpartition('/')[2]
+        ds_util.download_url(url, root=path, filename=filename)
+
+        full_archive_path = os.path.join(path, filename)
+
+        # Unpack zip archive
+        if full_archive_path.endswith(".zip"):
+            zip_ref = zipfile.ZipFile(full_archive_path, 'r')
+
+            if extract_parent:
+                zip_ref.extractall(os.path.dirname(path))
+            else:
+                zip_ref.extractall(path)
+
+            zip_ref.close()
+
+            os.remove(full_archive_path)
+
+    train_ds = ImageDirSource(train_path)
+    val_ds = ImageDirSource(valid_path)
+
+    return Source(
+        train_ds,
+        val_ds,
+    )
diff --git a/vel/math/__init__.py b/vel/data/source/nlp/__init__.py
similarity index 100%
rename from vel/math/__init__.py
rename to vel/data/source/nlp/__init__.py
diff --git a/vel/sources/nlp/imdb.py b/vel/data/source/nlp/imdb.py
similarity index 75%
rename from vel/sources/nlp/imdb.py
rename to vel/data/source/nlp/imdb.py
index 6a9310cb..badf59c7 100644
--- a/vel/sources/nlp/imdb.py
+++ b/vel/data/source/nlp/imdb.py
@@ -3,14 +3,14 @@
 import io
 import pickle
 
-import torchtext.datasets.imdb as imdb
 import torchtext.data as data
+import torchtext.datasets as ds
 
 
-from vel.api import TextData
+from vel.api import LanguageSource
 
 
-class IMDBCached(imdb.IMDB):
+class IMDBCached(ds.IMDB):
     """ Cached version of the IMDB dataset (to save time on tokenization) """
 
     def __init__(self, path, text_field, label_field, **kwargs):
@@ -45,9 +45,9 @@ def __init__(self, path, text_field, label_field, **kwargs):
         data.Dataset.__init__(self, examples, fields, **kwargs)
 
 
-def create(model_config, batch_size, vectors=None):
+def create(model_config, vocab_size: int, data_dir='imdb', vectors=None):
     """ Create an IMDB dataset """
-    path = model_config.data_dir('imdb')
+    path = model_config.data_dir(data_dir)
 
     text_field = data.Field(lower=True, tokenize='spacy', batch_first=True)
     label_field = data.LabelField(is_target=True)
@@ -58,16 +58,18 @@ def create(model_config, batch_size, vectors=None):
         label_field=label_field
     )
 
-    text_field.build_vocab(train_source, max_size=25_000, vectors=vectors)
+    text_field.build_vocab(train_source, max_size=vocab_size, vectors=vectors)
     label_field.build_vocab(train_source)
 
-    train_iterator, test_iterator = data.BucketIterator.splits(
-        (train_source, test_source),
-        batch_size=batch_size,
-        device=model_config.torch_device(),
-        shuffle=True
-    )
-
-    return TextData(
-        train_source, test_source, train_iterator, test_iterator, text_field, label_field
+    return LanguageSource(
+        train_source,
+        test_source,
+        fields=train_source.fields,
+        mapping={
+            'x': 'text',
+            'y': 'label'
+        },
+        metadata={
+            'alphabet_size': vocab_size+2
+        }
     )
diff --git a/vel/data/source/nlp/multi30k.py b/vel/data/source/nlp/multi30k.py
new file mode 100644
index 00000000..c52f5ae8
--- /dev/null
+++ b/vel/data/source/nlp/multi30k.py
@@ -0,0 +1,88 @@
+import io
+import os.path
+import pickle
+import re
+import spacy
+
+import torchtext.data as data
+import torchtext.datasets as ds
+
+from vel.api import SupervisedTextData
+
+
+class Multi30kCached(ds.Multi30k):
+    """ Cached version of the Multi30K dataset, to save time on tokenization every time """
+
+    def __init__(self, path, exts, fields, **kwargs):
+        # Each one is a
+        if os.path.isdir(path):
+            cache_file = os.path.join(path, '_cache.pk')
+        else:
+            cache_file = path + '_cache.pk'
+
+        if not isinstance(fields[0], (tuple, list)):
+            fields = [('src', fields[0]), ('trg', fields[1])]
+
+        if os.path.exists(cache_file):
+            with open(cache_file, 'rb') as fp:
+                examples = pickle.load(fp)
+        else:
+            src_path, trg_path = tuple(os.path.expanduser(path + x) for x in exts)
+
+            examples = []
+
+            with io.open(src_path, mode='r', encoding='utf-8') as src_file, \
+                    io.open(trg_path, mode='r', encoding='utf-8') as trg_file:
+                for src_line, trg_line in zip(src_file, trg_file):
+                    src_line, trg_line = src_line.strip(), trg_line.strip()
+                    if src_line != '' and trg_line != '':
+                        examples.append(data.Example.fromlist(
+                            [src_line, trg_line], fields))
+
+            with open(cache_file, 'wb') as fp:
+                pickle.dump(examples, file=fp)
+
+        data.Dataset.__init__(self, examples, fields, **kwargs)
+
+
+def create(model_config, batch_size, data_dir='wmt14'):
+    """ Create an Multi30k dataset. English-German """
+    path = model_config.data_dir(data_dir)
+
+    spacy_de = spacy.load('de')
+    spacy_en = spacy.load('en')
+
+    url = re.compile('(<url>.*</url>)')
+
+    def tokenize_de(text):
+        return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]
+
+    def tokenize_en(text):
+        return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]
+
+    en_field = data.Field(
+        lower=True, tokenize=tokenize_en, batch_first=True, init_token='<sos>', eos_token='<eos>'
+    )
+
+    de_field = data.Field(
+        lower=True, tokenize=tokenize_de, batch_first=True, init_token='<sos>', eos_token='<eos>'
+    )
+
+    train_source, val_source, test_source = Multi30kCached.splits(
+        root=path,
+        exts=('.en', '.de'),
+        fields=(en_field, de_field)
+    )
+
+    en_field.build_vocab(train_source.src, min_freq=2)
+    de_field.build_vocab(train_source.tgt, max_size=17_000)
+
+    train_iter, val_iter, test_iter = data.BucketIterator.splits(
+        (train_source, val_source, test_source),
+        batch_size=batch_size,
+        repeat=False
+    )
+
+    return SupervisedTextData(
+        train_source, val_source, train_iter, val_iter, en_field, de_field
+    )
diff --git a/vel/data/source/nlp/text_url.py b/vel/data/source/nlp/text_url.py
new file mode 100644
index 00000000..cadafecd
--- /dev/null
+++ b/vel/data/source/nlp/text_url.py
@@ -0,0 +1,95 @@
+import certifi
+import numpy as np
+import os
+import pathlib
+import urllib3
+
+import torch
+
+from vel.api import Source
+
+
+class TextUrlSource(Source):
+    """ Download text from source and model it character by character """
+
+    def __init__(self, url, absolute_data_path, train_val_split=0.8):
+        self.url = url
+        self.data_path = absolute_data_path
+        self.train_val_split = train_val_split
+
+        self.text_path = os.path.join(self.data_path, 'text.txt')
+        self.processed_path = os.path.join(self.data_path, 'processed.data')
+
+        self.data_dict = self.download()
+
+        content_encoded = self.data_dict['content_encoded']
+
+        split_idx = int(len(content_encoded) * train_val_split)
+
+        super().__init__(
+            train=content_encoded[:split_idx],
+            validation=content_encoded[split_idx:],
+            metadata={
+                'alphabet': self.data_dict['alphabet'],
+                'character_to_index': self.data_dict['character_to_index'],
+                'index_to_character': self.data_dict['index_to_character']
+            }
+        )
+
+    def encode_character(self, char):
+        return self.data_dict['character_to_index'][char]
+
+    def decode_character(self, index):
+        return self.data_dict['index_to_character'][index]
+
+    def download(self) -> dict:
+        """ Make sure data file is downloaded and stored properly """
+        if not os.path.exists(self.data_path):
+            # Create if it doesn't exist
+            pathlib.Path(self.data_path).mkdir(parents=True, exist_ok=True)
+
+        if not os.path.exists(self.text_path):
+            http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED', ca_certs=certifi.where())
+
+            with open(self.text_path, 'wt') as fp:
+                request = http.request('GET', self.url)
+                content = request.data.decode('utf8')
+                fp.write(content)
+
+        if not os.path.exists(self.processed_path):
+            with open(self.text_path, 'rt') as fp:
+                content = fp.read()
+
+            alphabet = sorted(set(content))
+
+            index_to_character = {idx: c for idx, c in enumerate(alphabet, 1)}
+            character_to_index = {c: idx for idx, c in enumerate(alphabet, 1)}
+
+            content_encoded = np.array([character_to_index[c] for c in content], dtype=np.uint8)
+
+            data_dict = {
+                'alphabet': alphabet,
+                'index_to_character': index_to_character,
+                'character_to_index': character_to_index,
+                'content_encoded': content_encoded
+            }
+
+            with open(self.processed_path, 'wb') as fp:
+                torch.save(data_dict, fp)
+        else:
+            with open(self.processed_path, 'rb') as fp:
+                data_dict = torch.load(fp)
+
+        return data_dict
+
+
+def create(model_config, url, local_dir, train_val_split=0.8):
+    """ Vel factory function """
+    if not os.path.isabs(local_dir):
+        local_dir = model_config.data_dir(local_dir)
+
+    return TextUrlSource(
+        url,
+        absolute_data_path=local_dir,
+        train_val_split=train_val_split,
+    )
diff --git a/vel/data/source/nlp/wmt14.py b/vel/data/source/nlp/wmt14.py
new file mode 100644
index 00000000..1d1f87f8
--- /dev/null
+++ b/vel/data/source/nlp/wmt14.py
@@ -0,0 +1,88 @@
+import io
+import os.path
+import pickle
+import re
+import spacy
+
+import torchtext.data as data
+import torchtext.datasets as ds
+
+from vel.api import SupervisedTextData
+
+
+class WMT14Cached(ds.WMT14):
+    """ Cached version of the WMT14 dataset, to save time on tokenization every time """
+
+    def __init__(self, path, exts, fields, **kwargs):
+        # Each one is a
+        if os.path.isdir(path):
+            cache_file = os.path.join(path, '_cache.pk')
+        else:
+            cache_file = path + '_cache.pk'
+
+        if not isinstance(fields[0], (tuple, list)):
+            fields = [('src', fields[0]), ('trg', fields[1])]
+
+        if os.path.exists(cache_file):
+            with open(cache_file, 'rb') as fp:
+                examples = pickle.load(fp)
+        else:
+            src_path, trg_path = tuple(os.path.expanduser(path + x) for x in exts)
+
+            examples = []
+
+            with io.open(src_path, mode='r', encoding='utf-8') as src_file, \
+                    io.open(trg_path, mode='r', encoding='utf-8') as trg_file:
+                for src_line, trg_line in zip(src_file, trg_file):
+                    src_line, trg_line = src_line.strip(), trg_line.strip()
+                    if src_line != '' and trg_line != '':
+                        examples.append(data.Example.fromlist(
+                            [src_line, trg_line], fields))
+
+            with open(cache_file, 'wb') as fp:
+                pickle.dump(examples, file=fp)
+
+        data.Dataset.__init__(self, examples, fields, **kwargs)
+
+
+def create(model_config, batch_size, data_dir='wmt14'):
+    """ Create an WMT14 dataset. English-German """
+    path = model_config.data_dir(data_dir)
+
+    spacy_de = spacy.load('de')
+    spacy_en = spacy.load('en')
+
+    url = re.compile('(<url>.*</url>)')
+
+    def tokenize_de(text):
+        return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]
+
+    def tokenize_en(text):
+        return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]
+
+    en_field = data.Field(
+        lower=True, tokenize=tokenize_en, batch_first=True, init_token='<sos>', eos_token='<eos>'
+    )
+
+    de_field = data.Field(
+        lower=True, tokenize=tokenize_de, batch_first=True, init_token='<sos>', eos_token='<eos>'
+    )
+
+    train_source, val_source, test_source = WMT14Cached.splits(
+        root=path,
+        exts=('.en', '.de'),
+        fields=(en_field, de_field)
+    )
+
+    en_field.build_vocab(train_source.src, min_freq=2)
+    de_field.build_vocab(train_source.tgt, max_size=17_000)
+
+    train_iter, val_iter, test_iter = data.BucketIterator.splits(
+        (train_source, val_source, test_source),
+        batch_size=batch_size,
+        repeat=False
+    )
+
+    return SupervisedTextData(
+        train_source, val_source, train_iter, val_iter, en_field, de_field
+    )
diff --git a/vel/metrics/__init__.py b/vel/data/source/vision/__init__.py
similarity index 100%
rename from vel/metrics/__init__.py
rename to vel/data/source/vision/__init__.py
diff --git a/vel/data/source/vision/cifar10.py b/vel/data/source/vision/cifar10.py
new file mode 100644
index 00000000..17641b59
--- /dev/null
+++ b/vel/data/source/vision/cifar10.py
@@ -0,0 +1,43 @@
+from torchvision import datasets
+
+from vel.api import Source
+
+
+def create(model_config):
+    """
+    Create a CIFAR10 dataset, normalized.
+    Augmentations are the same as in the literature benchmarking CIFAR performance.
+    """
+    path = model_config.data_dir('cifar10')
+
+    train_dataset = datasets.CIFAR10(path, train=True, download=True)
+    test_dataset = datasets.CIFAR10(path, train=False, download=True)
+
+    train_data = train_dataset.data
+    mean_value = (train_data / 255).mean(axis=(0, 1, 2))
+    std_value = (train_data / 255).std(axis=(0, 1, 2))
+
+    return Source(
+        train=train_dataset,
+        validation=test_dataset,
+        metadata={
+            'train_mean': mean_value,
+            'train_std': std_value
+        }
+    )
+
+    # augmentations = [ToArray()] + (augmentations if augmentations is not None else [])
+
+    # if normalize:
+    #
+    #     augmentations.append(Normalize(mean=mean_value, std=std_value, tags=['train', 'val']))
+    #
+    # augmentations.append(ToTensor())
+    #
+    # return SupervisedTrainingData(
+    #     train_dataset,
+    #     test_dataset,
+    #     batch_size=batch_size,
+    #     num_workers=num_workers,
+    #     augmentations=augmentations
+    # )
diff --git a/vel/data/source/vision/mnist.py b/vel/data/source/vision/mnist.py
new file mode 100644
index 00000000..58f8ee90
--- /dev/null
+++ b/vel/data/source/vision/mnist.py
@@ -0,0 +1,24 @@
+from torchvision import datasets
+
+from vel.api import Source
+
+
+def create(model_config):
+    """ Create a MNIST dataset """
+    path = model_config.data_dir('mnist')
+
+    train_dataset = datasets.MNIST(path, train=True, download=True)
+    test_dataset = datasets.MNIST(path, train=False, download=True)
+
+    train_data = train_dataset.data
+    mean_value = (train_data.double() / 255).mean().item()
+    std_value = (train_data.double() / 255).std().item()
+
+    return Source(
+        train=train_dataset,
+        validation=test_dataset,
+        metadata={
+            'train_mean': mean_value,
+            'train_std': std_value
+        }
+    )
diff --git a/vel/data/source/vision/omniglot.py b/vel/data/source/vision/omniglot.py
new file mode 100644
index 00000000..659f53b4
--- /dev/null
+++ b/vel/data/source/vision/omniglot.py
@@ -0,0 +1,16 @@
+from torchvision import datasets
+
+from vel.api import Source
+
+
+def create(model_config):
+    """ Create an Omniglot dataset """
+    path = model_config.data_dir('omniglot')
+
+    train_dataset = datasets.Omniglot(path, background=True, download=True)
+    test_dataset = datasets.Omniglot(path, background=False, download=True)
+
+    return Source(
+        train=train_dataset,
+        validation=test_dataset,
+    )
diff --git a/vel/models/__init__.py b/vel/data/transformation/__init__.py
similarity index 100%
rename from vel/models/__init__.py
rename to vel/data/transformation/__init__.py
diff --git a/vel/data/transformation/binarize_image.py b/vel/data/transformation/binarize_image.py
new file mode 100644
index 00000000..38c13610
--- /dev/null
+++ b/vel/data/transformation/binarize_image.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+import vel.api as api
+
+
+class BinarizeImage(api.ScopedTransformation):
+    """ Convert [0,1] image into a binary {0, 1} representation """
+
+    def transform(self, x_data):
+        # Sample image from a Bernoulli distribution
+        return np.random.binomial(1, x_data).astype(np.float32)
+
+
+def create(scope='x', tags=None):
+    """ Vel factory function """
+    return BinarizeImage(scope, tags)
diff --git a/vel/data/transformation/image_to_tensor.py b/vel/data/transformation/image_to_tensor.py
new file mode 100644
index 00000000..aae7b486
--- /dev/null
+++ b/vel/data/transformation/image_to_tensor.py
@@ -0,0 +1,36 @@
+import numpy as np
+
+import torchvision.transforms.functional as F
+
+from vel.api.transformation import ScopedTransformation
+
+
+class ImageToTensor(ScopedTransformation):
+    """ Convert image array to a tensor """
+    def transform(self, value):
+        # First let's make sure it's actually a numpy array
+        value = np.asarray(value)
+
+        if value.dtype == np.uint8:
+            value = value.astype(np.float32) / 255.0
+
+        value = value.astype(np.float32)
+
+        if len(value.shape) == 2:
+            # If the image has only one channel, it still needs to be specified
+            value = value.reshape(value.shape[0], value.shape[1], 1)
+
+        return F.to_tensor(value)
+
+    def denormalization_transform(self, value):
+        image_array = np.transpose(value.numpy(), (1, 2, 0))
+
+        if len(image_array.shape) == 3 and image_array.shape[-1] == 1:
+            return image_array[:, :, 0]
+
+        return image_array
+
+
+def create(mode='x', tags=None):
+    """ Vel factory function """
+    return ImageToTensor(mode, tags)
diff --git a/vel/data/transformation/normalize.py b/vel/data/transformation/normalize.py
new file mode 100644
index 00000000..a29ad225
--- /dev/null
+++ b/vel/data/transformation/normalize.py
@@ -0,0 +1,39 @@
+import numpy as np
+
+import vel.api as api
+
+
+class Normalize(api.ScopedTransformation):
+    """ Normalize input mean and standard deviation """
+
+    def __init__(self, mean=None, std=None, scope='x', tags=None):
+        super().__init__(scope, tags)
+
+        self.mean = mean
+        self.std = std
+
+        if self.mean is not None:
+            self.mean = np.asarray(self.mean)
+
+        if self.std is not None:
+            self.std = np.asarray(self.std)
+
+    def initialize(self, source):
+        """ Initialize transformation from source """
+        if self.mean is None:
+            self.mean = source.metadata['train_mean']
+
+        if self.std is None:
+            self.std = source.metadata['train_std']
+
+    def transform(self, value):
+        return (value - self.mean) / self.std
+
+    def denormalization_transform(self, value):
+        """ Operation reverse to normalization """
+        return value * self.std + self.mean
+
+
+def create(mean=None, std=None, mode='x', tags=None):
+    """ Vel factory function """
+    return Normalize(mean=mean, std=std, scope=mode, tags=tags)
diff --git a/vel/data/transformation/pil_resize.py b/vel/data/transformation/pil_resize.py
new file mode 100644
index 00000000..326a34b2
--- /dev/null
+++ b/vel/data/transformation/pil_resize.py
@@ -0,0 +1,18 @@
+import PIL.Image as Image
+
+import vel.api as api
+
+
+class PilResize(api.ScopedTransformation):
+    """ Scale the PIL image """
+    def __init__(self, shape, scope='x', tags=None):
+        super().__init__(scope, tags)
+        self.shape = shape
+
+    def transform(self, x_data):
+        return x_data.resize(self.shape, Image.LANCZOS)
+
+
+def create(shape, scope='x', tags=None):
+    """ Vel factory function """
+    return PilResize(shape, scope, tags)
diff --git a/vel/data/transformation/to_array.py b/vel/data/transformation/to_array.py
new file mode 100644
index 00000000..8c1e838c
--- /dev/null
+++ b/vel/data/transformation/to_array.py
@@ -0,0 +1,19 @@
+import numpy as np
+
+from vel.api.transformation import ScopedTransformation
+
+
+class ToArray(ScopedTransformation):
+    """ Convert image to an array of floats """
+
+    def transform(self, value):
+        array = np.array(value)
+
+        if array.dtype == np.uint8:
+            return array.astype(np.float32) / 255.0
+        else:
+            return array
+
+
+def create(mode='x', tags=None):
+    return ToArray(mode, tags)
diff --git a/vel/data/transformation/unsupervised.py b/vel/data/transformation/unsupervised.py
new file mode 100644
index 00000000..7af7e560
--- /dev/null
+++ b/vel/data/transformation/unsupervised.py
@@ -0,0 +1,12 @@
+from vel.api import Transformation
+
+
+class Unsupervised(Transformation):
+    """ Simply transform supervised to an unsupervised dataset, cloning data to a target """
+    def __call__(self, datapoint):
+        datapoint['y'] = datapoint['x']
+        return datapoint
+
+
+def create():
+    return Unsupervised()
diff --git a/vel/exceptions.py b/vel/exception.py
similarity index 100%
rename from vel/exceptions.py
rename to vel/exception.py
diff --git a/vel/models/imagenet/__init__.py b/vel/function/__init__.py
similarity index 100%
rename from vel/models/imagenet/__init__.py
rename to vel/function/__init__.py
diff --git a/vel/schedules/constant.py b/vel/function/constant.py
similarity index 100%
rename from vel/schedules/constant.py
rename to vel/function/constant.py
diff --git a/vel/schedules/linear.py b/vel/function/linear.py
similarity index 93%
rename from vel/schedules/linear.py
rename to vel/function/linear.py
index ef74b03c..58ca5a23 100644
--- a/vel/schedules/linear.py
+++ b/vel/function/linear.py
@@ -1,4 +1,4 @@
-import vel.util.intepolate as interpolate
+import vel.util.interpolate as interpolate
 
 from vel.api import Schedule
 
@@ -18,4 +18,3 @@ def value(self, progress_indicator):
 def create(initial_value, final_value):
     """ Vel factory function """
     return LinearSchedule(initial_value, final_value)
-
diff --git a/vel/schedules/linear_and_constant.py b/vel/function/linear_and_constant.py
similarity index 95%
rename from vel/schedules/linear_and_constant.py
rename to vel/function/linear_and_constant.py
index f04b9e4a..fecf5d19 100644
--- a/vel/schedules/linear_and_constant.py
+++ b/vel/function/linear_and_constant.py
@@ -1,4 +1,4 @@
-import vel.util.intepolate as interpolate
+import vel.util.interpolate as interpolate
 
 from vel.api import Schedule
 
diff --git a/vel/models/rnn/__init__.py b/vel/internal/__init__.py
similarity index 100%
rename from vel/models/rnn/__init__.py
rename to vel/internal/__init__.py
diff --git a/vel/internals/context.py b/vel/internal/context.py
similarity index 100%
rename from vel/internals/context.py
rename to vel/internal/context.py
diff --git a/vel/internals/generic_factory.py b/vel/internal/generic_factory.py
similarity index 100%
rename from vel/internals/generic_factory.py
rename to vel/internal/generic_factory.py
diff --git a/vel/internals/parser.py b/vel/internal/parser.py
similarity index 98%
rename from vel/internals/parser.py
rename to vel/internal/parser.py
index 6c79e10d..d26f000a 100644
--- a/vel/internals/parser.py
+++ b/vel/internal/parser.py
@@ -1,7 +1,7 @@
 import os
 import yaml
 
-from vel.exceptions import VelException
+from vel.exception import VelException
 
 
 class Dummy:
diff --git a/vel/internals/provider.py b/vel/internal/provider.py
similarity index 91%
rename from vel/internals/provider.py
rename to vel/internal/provider.py
index 526181cc..6a6950d0 100644
--- a/vel/internals/provider.py
+++ b/vel/internal/provider.py
@@ -1,13 +1,15 @@
 import importlib
 import inspect
+import typing
 
-from vel.internals.parser import Variable
-from vel.internals.generic_factory import GenericFactory
+from vel.internal.parser import Variable
+from vel.internal.generic_factory import GenericFactory
 
 
 class Provider:
     """ Dependency injection resolver for the configuration file """
-    def __init__(self, environment, instances=None, parameters=None):
+    def __init__(self, environment: dict, instances: typing.Optional[dict] = None,
+                 parameters: typing.Optional[dict] = None):
         self.environment = environment
 
         self.parameters = parameters if parameters is not None else {}
@@ -76,11 +78,12 @@ def instantiate_from_data(self, object_data):
         else:
             return object_data
 
-    def render_configuration(self, configuration=None):
-        """ Render variables in configuration object but don't instantiate anything """
-        if configuration is None:
-            configuration = self.environment
+    def render_environment(self):
+        """ Render variables in current environment """
+        return self.render_configuration(self.environment)
 
+    def render_configuration(self, configuration):
+        """ Render variables in configuration object but don't instantiate anything """
         if isinstance(configuration, dict):
             return {k: self.render_configuration(v) for k, v in configuration.items()}
         elif isinstance(configuration, list):
diff --git a/vel/models/vision/__init__.py b/vel/internal/test/__init__.py
similarity index 100%
rename from vel/models/vision/__init__.py
rename to vel/internal/test/__init__.py
diff --git a/vel/internals/tests/fixture_a.py b/vel/internal/test/fixture_a.py
similarity index 100%
rename from vel/internals/tests/fixture_a.py
rename to vel/internal/test/fixture_a.py
diff --git a/vel/internals/tests/fixture_b.py b/vel/internal/test/fixture_b.py
similarity index 100%
rename from vel/internals/tests/fixture_b.py
rename to vel/internal/test/fixture_b.py
diff --git a/vel/internals/tests/test_parser.py b/vel/internal/test/test_parser.py
similarity index 98%
rename from vel/internals/tests/test_parser.py
rename to vel/internal/test/test_parser.py
index 67fdf2d5..d2fc7053 100644
--- a/vel/internals/tests/test_parser.py
+++ b/vel/internal/test/test_parser.py
@@ -1,6 +1,6 @@
 import pytest
 
-import vel.internals.parser as v
+import vel.internal.parser as v
 
 
 @pytest.fixture
diff --git a/vel/internals/tests/test_provider.py b/vel/internal/test/test_provider.py
similarity index 74%
rename from vel/internals/tests/test_provider.py
rename to vel/internal/test/test_provider.py
index 4f49e675..594fa74a 100644
--- a/vel/internals/tests/test_provider.py
+++ b/vel/internal/test/test_provider.py
@@ -1,9 +1,9 @@
 import os
 import pytest
 
-import vel.internals.provider as v
-import vel.internals.parser as p
-import vel.exceptions as e
+import vel.internal.provider as v
+import vel.internal.parser as p
+import vel.exception as e
 
 
 def data_function(a, b):
@@ -37,17 +37,17 @@ def test_simple_injection():
         'a': 1,
         'b': 2,
         'one': {
-            'name': 'vel.internals.tests.fixture_a'
+            'name': 'vel.internal.test.fixture_a'
         },
 
         'two': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'a': 5,
             'b': 6
         },
 
         'three': {
-            'name': 'vel.internals.tests.fixture_b',
+            'name': 'vel.internal.test.fixture_b',
             'd': 'd'
         }
     })
@@ -78,20 +78,20 @@ def test_parameter_resolution():
         'a': 1,
         'b': p.Parameter("xxx"),
         'one': {
-            'name': 'vel.internals.tests.fixture_a'
+            'name': 'vel.internal.test.fixture_a'
         },
         'two': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': p.Parameter('yyy')
         },
 
         'three': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': p.Parameter('yyy', 7)
         },
 
         'four': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': p.EnvironmentVariable('TEST_VAR')
         },
 
@@ -120,45 +120,45 @@ def test_render_configuration():
         'a': 1,
         'b': p.Parameter("xxx"),
         'one': {
-            'name': 'vel.internals.tests.fixture_a'
+            'name': 'vel.internal.test.fixture_a'
         },
         'two': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': p.Parameter('yyy', 5)
         },
 
         'three': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': p.Parameter('yyy', 7)
         },
 
         'four': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': p.EnvironmentVariable('TEST_VAR')
         },
 
     }, parameters={'xxx': 5})
 
-    configuration = provider.render_configuration()
+    configuration = provider.render_environment()
 
     assert configuration == {
         'a': 1,
         'b': 5,
         'one': {
-            'name': 'vel.internals.tests.fixture_a'
+            'name': 'vel.internal.test.fixture_a'
         },
         'two': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': 5
         },
 
         'three': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': 7
         },
 
         'four': {
-            'name': 'vel.internals.tests.fixture_a',
+            'name': 'vel.internal.test.fixture_a',
             'b': '10'
         },
     }
diff --git a/vel/internals/model_config.py b/vel/internals/model_config.py
deleted file mode 100644
index 979fcdee..00000000
--- a/vel/internals/model_config.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import datetime as dtm
-import os.path
-
-from vel.exceptions import VelInitializationException
-from vel.internals.parser import Parser
-from vel.internals.provider import Provider
-
-
-class ModelConfig:
-    """
-    Read from YAML configuration of a model, specifying all details of the run.
-    Is a frontend for the provider, resolving all dependency-injection requests.
-    """
-
-    PROJECT_FILE_NAME = '.velproject.yaml'
-
-    @staticmethod
-    def find_project_directory(start_path) -> str:
-        """ Locate top-level project directory  """
-        start_path = os.path.realpath(start_path)
-        possible_name = os.path.join(start_path, ModelConfig.PROJECT_FILE_NAME)
-
-        if os.path.exists(possible_name):
-            return start_path
-        else:
-            up_path = os.path.realpath(os.path.join(start_path, '..'))
-            if os.path.realpath(start_path) == up_path:
-                raise RuntimeError(f"Couldn't find project file starting from {start_path}")
-            else:
-                return ModelConfig.find_project_directory(up_path)
-
-    @classmethod
-    def from_file(cls, filename: str, run_number: int, continue_training: bool = False, seed: int = None,
-                  device: str = 'cuda', params=None):
-        """ Create model config from file """
-        with open(filename, 'r') as fp:
-            model_config_contents = Parser.parse(fp)
-
-        project_config_path = ModelConfig.find_project_directory(os.path.dirname(os.path.abspath(filename)))
-
-        with open(os.path.join(project_config_path, cls.PROJECT_FILE_NAME), 'r') as fp:
-            project_config_contents = Parser.parse(fp)
-
-        aggregate_dictionary = {
-            **project_config_contents,
-            **model_config_contents
-        }
-
-        return ModelConfig(
-            filename=filename,
-            configuration=aggregate_dictionary,
-            run_number=run_number,
-            project_dir=project_config_path,
-            continue_training=continue_training,
-            seed=seed,
-            device=device,
-            parameters=params
-        )
-
-    @classmethod
-    def from_memory(cls, model_data: dict, run_number: int, project_dir: str,
-                    continue_training=False, seed: int = None, device: str = 'cuda', params=None):
-        """ Create model config from supplied data """
-        return ModelConfig(
-            filename="[memory]",
-            configuration=model_data,
-            run_number=run_number,
-            project_dir=project_dir,
-            continue_training=continue_training,
-            seed=seed,
-            device=device,
-            parameters=params
-        )
-
-    def __init__(self, filename: str, configuration: dict, run_number: int, project_dir: str,
-                 continue_training=False, seed: int = None, device: str = 'cuda', parameters=None):
-        self.filename = filename
-        self.device = device
-        self.continue_training = continue_training
-        self.run_number = run_number
-        self.seed = seed if seed is not None else (dtm.date.today().year + self.run_number)
-
-        self.contents = configuration
-        self.project_dir = os.path.normpath(project_dir)
-
-        self.command_descriptors = self.contents.get('commands', [])
-
-        # This one is special and needs to get removed
-        if 'commands' in self.contents:
-            del self.contents['commands']
-
-        self.provider = Provider(self._prepare_environment(), {'model_config': self}, parameters=parameters)
-        self._model_name = self.provider.get("name")
-
-    def _prepare_environment(self) -> dict:
-        """ Return full environment for dependency injection """
-        return {**self.contents, 'run_number': self.run_number}
-
-    def render_configuration(self) -> dict:
-        """ Return a nice and picklable run configuration """
-        return self.provider.render_configuration()
-
-    ####################################################################################################################
-    # COMMAND UTILITIES
-    def get_command(self, command_name):
-        """ Return object for given command """
-        return self.provider.instantiate_from_data(self.command_descriptors[command_name])
-
-    def run_command(self, command_name, varargs):
-        """ Instantiate model class """
-        command_descriptor = self.get_command(command_name)
-        return command_descriptor.run(*varargs)
-
-    ####################################################################################################################
-    # MODEL DIRECTORIES
-    def checkpoint_dir(self, *args) -> str:
-        """ Return checkpoint directory for this model """
-        return self.output_dir('checkpoints', self.run_name, *args)
-
-    def data_dir(self, *args) -> str:
-        """ Return data directory for given dataset """
-        return self.project_data_dir(*args)
-
-    def openai_dir(self) -> str:
-        """ Return directory for openai output files for this model """
-        return self.output_dir('openai', self.run_name)
-
-    def project_data_dir(self, *args) -> str:
-        """ Directory where to store data """
-        return os.path.normpath(os.path.join(self.project_dir, 'data', *args))
-
-    def output_dir(self, *args) -> str:
-        """ Directory where to store output """
-        return os.path.join(self.project_dir, 'output', *args)
-
-    def project_top_dir(self, *args) -> str:
-        """ Project top-level directory """
-        return os.path.join(self.project_dir, *args)
-
-    ####################################################################################################################
-    # NAME UTILITIES
-    @property
-    def run_name(self) -> str:
-        """ Return name of the run """
-        return "{}/{}".format(self._model_name, self.run_number)
-
-    @property
-    def name(self) -> str:
-        """ Return name of the model """
-        return self._model_name
-
-    ####################################################################################################################
-    # MISC GETTERS
-    def torch_device(self):
-        """ Return torch device object """
-        import torch
-        return torch.device(self.device)
-
-    ####################################################################################################################
-    # PROVIDER API
-    def provide(self, name):
-        """ Return a dependency-injected instance """
-        return self.provider.instantiate_by_name(name)
-
-    def provide_with_default(self, name, default=None):
-        """ Return a dependency-injected instance """
-        return self.provider.instantiate_by_name_with_default(name, default_value=default)
-
-    ####################################################################################################################
-    # BANNERS - Maybe shouldn't be here, but they are for now
-    def banner(self, command_name) -> None:
-        """ Print a banner for running the system """
-        import torch
-        device = self.torch_device()
-
-        print("=" * 80)
-        print(f"Pytorch version: {torch.__version__} cuda version {torch.version.cuda} cudnn version {torch.backends.cudnn.version()}")
-        print("Running model {}, run {} -- command {} -- device {}".format(self._model_name, self.run_number, command_name, self.device))
-        if device.type == 'cuda':
-            device_idx = 0 if device.index is None else device.index
-            print(f"CUDA Device name {torch.cuda.get_device_name(device_idx)}")
-        print(dtm.datetime.now().strftime("%Y/%m/%d - %H:%M:%S"))
-        print("=" * 80)
-
-    def quit_banner(self) -> None:
-        """ Print a banner for running the system """
-        print("=" * 80)
-        print("Done.")
-        print(dtm.datetime.now().strftime("%Y/%m/%d - %H:%M:%S"))
-        print("=" * 80)
-
-    ####################################################################################################################
-    # Small UI utils
-    def __repr__(self):
-        return f"<ModelConfig at {self.filename}>"
diff --git a/vel/launcher.py b/vel/launcher.py
index e2a00a47..cd3a08de 100644
--- a/vel/launcher.py
+++ b/vel/launcher.py
@@ -3,8 +3,8 @@
 import multiprocessing
 import sys
 
-from vel.internals.model_config import ModelConfig
-from vel.internals.parser import Parser
+from vel.api.model_config import ModelConfig
+from vel.internal.parser import Parser
 
 
 def main():
@@ -13,16 +13,18 @@ def main():
 
     parser.add_argument('config', metavar='FILENAME', help='Configuration file for the run')
     parser.add_argument('command', metavar='COMMAND', help='A command to run')
-    parser.add_argument('varargs', nargs='*', metavar='VARARGS', help='Extra options to the command')
+    parser.add_argument('varargs', nargs='*', default=[], metavar='VARARGS', help='Extra options to the command')
     parser.add_argument('-r', '--run_number', type=int, default=0, help="A run number")
     parser.add_argument('-d', '--device', default='cuda', help="A device to run the model on")
     parser.add_argument('-s', '--seed', type=int, default=None, help="Random seed for the project")
+    parser.add_argument('-t', '--tag', type=str, default=None, help="String tag for a given run")
+    parser.add_argument('--werr', action='store_true', default=False, help="Convert warnings to errors")
     parser.add_argument(
         '-p', '--param', type=str, metavar='NAME=VALUE', action='append', default=[],
         help="Configuration parameters"
     )
     parser.add_argument(
-        '--continue', action='store_true', default=False, help="Continue previously started learning process"
+        '--resume', action='store_true', default=False, help="Resume previously started learning process"
     )
     parser.add_argument(
         '--profile', type=str, default=None, help="Profiler output"
@@ -30,9 +32,14 @@ def main():
 
     args = parser.parse_args()
 
+    if args.werr:
+        import warnings
+        warnings.filterwarnings('error', module='vel.*')
+        warnings.filterwarnings('error', module='torch\\..*')
+
     model_config = ModelConfig.from_file(
-        args.config, args.run_number, continue_training=getattr(args, 'continue'), device=args.device, seed=args.seed,
-        params={k: v for (k, v) in (Parser.parse_equality(eq) for eq in args.param)}
+        args.config, args.run_number, resume_training=args.resume, device=args.device, seed=args.seed,
+        parameters={k: v for (k, v) in (Parser.parse_equality(eq) for eq in args.param)}, tag=args.tag
     )
 
     if model_config.project_dir not in sys.path:
@@ -44,10 +51,7 @@ def main():
         # This needs to be called before any of PyTorch module is imported
         multiprocessing.set_start_method(multiprocessing_setting)
 
-    # Set seed already in the launcher
-    from vel.util.random import set_seed
-    set_seed(model_config.seed)
-
+    model_config.set_seed()
     model_config.banner(args.command)
 
     if args.profile:
diff --git a/vel/metric/__init__.py b/vel/metric/__init__.py
new file mode 100644
index 00000000..244c8acd
--- /dev/null
+++ b/vel/metric/__init__.py
@@ -0,0 +1,6 @@
+from .base.base_metric import BaseMetric, MetricKey  # noqa
+from .base.averaging_metric import (
+    AveragingMetric, AveragingNamedMetric, AveragingSupervisedMetric, DefaultAveragingNamedMetric  # noqa
+)
+from .base.value_metric import ValueMetric  # noqa
+from .base.image_metric import RandomImageMetric  # noqa
diff --git a/vel/metrics/accuracy.py b/vel/metric/accuracy.py
similarity index 73%
rename from vel/metrics/accuracy.py
rename to vel/metric/accuracy.py
index 8cb332bf..1416e61a 100644
--- a/vel/metrics/accuracy.py
+++ b/vel/metric/accuracy.py
@@ -1,10 +1,10 @@
-from vel.api.metrics.averaging_metric import AveragingSupervisedMetric
+from vel.metric.base.averaging_metric import AveragingSupervisedMetric
 
 
 class Accuracy(AveragingSupervisedMetric):
     """ Classification accuracy """
-    def __init__(self):
-        super().__init__("accuracy")
+    def __init__(self, scope="train"):
+        super().__init__("accuracy", scope=scope)
 
     def _value_function(self, x_input, y_true, y_pred):
         """ Return classification accuracy of input """
diff --git a/vel/metric/base/__init__.py b/vel/metric/base/__init__.py
new file mode 100644
index 00000000..a50c4f49
--- /dev/null
+++ b/vel/metric/base/__init__.py
@@ -0,0 +1,4 @@
+from .averaging_metric import AveragingSupervisedMetric, AveragingNamedMetric, AveragingMetric, DefaultAveragingNamedMetric
+from .base_metric import BaseMetric, MetricKey
+from .summing_metric import SummingMetric, SummingNamedMetric
+from .value_metric import ValueMetric
diff --git a/vel/api/metrics/averaging_metric.py b/vel/metric/base/averaging_metric.py
similarity index 67%
rename from vel/api/metrics/averaging_metric.py
rename to vel/metric/base/averaging_metric.py
index 37b667f3..ae535fa7 100644
--- a/vel/api/metrics/averaging_metric.py
+++ b/vel/metric/base/averaging_metric.py
@@ -5,8 +5,8 @@
 
 class AveragingMetric(BaseMetric):
     """ Base class for metrics that simply calculate the average over the epoch """
-    def __init__(self, name):
-        super().__init__(name)
+    def __init__(self, name, scope="general"):
+        super().__init__(name, scope=scope)
 
         self.storage = []
 
@@ -29,17 +29,30 @@ def value(self):
 
 class AveragingNamedMetric(AveragingMetric):
     """ Super simple averaging metric that just takes a value from dictionary and averages it over samples """
-    def __init__(self, name):
-        super().__init__(name)
+    def __init__(self, name, scope="general"):
+        super().__init__(name, scope=scope)
 
     def _value_function(self, batch_info):
         return batch_info[self.name]
 
 
+class DefaultAveragingNamedMetric(AveragingNamedMetric):
+    """ AveragingNamedMetric that has a default value in case a metric is not found in the batch """
+    def __init__(self, name, scope="general", defaut_value=0.0):
+        super().__init__(name, scope=scope)
+        self.default_value = defaut_value
+
+    def _value_function(self, batch_info):
+        if self.name not in batch_info:
+            return self.default_value
+        else:
+            return batch_info[self.name]
+
+
 class AveragingSupervisedMetric(BaseMetric):
     """ Base class for metrics that simply calculate the average over the epoch """
-    def __init__(self, name):
-        super().__init__(name)
+    def __init__(self, name, scope="general"):
+        super().__init__(name, scope=scope)
 
         self.storage = []
 
diff --git a/vel/api/metrics/base_metric.py b/vel/metric/base/base_metric.py
similarity index 51%
rename from vel/api/metrics/base_metric.py
rename to vel/metric/base/base_metric.py
index d8d12523..5ff1c10a 100644
--- a/vel/api/metrics/base_metric.py
+++ b/vel/metric/base/base_metric.py
@@ -1,11 +1,31 @@
+import attr
+import typing
+
 from vel.api import TrainingInfo
 
 
+@attr.s(auto_attribs=True, frozen=True)
+class MetricKey:
+    """ Key for each metric """
+    name: str
+    scope: str
+    dataset: typing.Optional[str] = None
+    metric_type: str = 'scalar'
+
+    def format(self):
+        """ Format a metric key into a string """
+        if self.dataset is None:
+            return f"{self.scope}/{self.name}"
+        else:
+            return f"{self.dataset}:{self.scope}/{self.name}"
+
+
 class BaseMetric:
     """ Base class for all the metrics """
 
-    def __init__(self, name):
+    def __init__(self, name, scope="general"):
         self.name = name
+        self.scope = scope
 
     def calculate(self, batch_info):
         """ Calculate value of a metric based on supplied data """
@@ -23,6 +43,15 @@ def write_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict)
         """ Potentially store some metric state to the checkpoint """
         pass
 
+    def prefix(self, prefix: str):
+        """ Prepend a prefix to the name of the metric """
+        self.name = f"{prefix}.{self.name}"
+        return self
+
     def load_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict) -> None:
         """ Potentially load some metric state from the checkpoint """
         pass
+
+    def metric_type(self) -> str:
+        """ Type of the metric """
+        return 'scalar'
diff --git a/vel/metric/base/image_metric.py b/vel/metric/base/image_metric.py
new file mode 100644
index 00000000..c4ca40da
--- /dev/null
+++ b/vel/metric/base/image_metric.py
@@ -0,0 +1,40 @@
+import numpy as np
+
+from .base_metric import BaseMetric
+
+
+class ImageMetric(BaseMetric):
+    """ Metric that logs an image """
+
+    def metric_type(self) -> str:
+        return 'image'
+
+
+class RandomImageMetric(ImageMetric):
+    """ Just pick a random image from the supplied list """
+
+    def __init__(self, name, scope="general"):
+        super().__init__(name, scope=scope)
+
+        self.image = None
+
+    def calculate(self, batch_info):
+        batch = batch_info[self.name]
+
+        if batch is not None:
+            if len(batch.shape) > 3:
+                image = batch[np.random.choice(batch.shape[0])]
+            else:
+                image = batch
+
+            if image.shape[2] == 1:
+                image = np.broadcast_to(image, shape=(image.shape[0], image.shape[1], 3))
+
+            self.image = image
+
+    def reset(self):
+        self.image = None
+
+    def value(self):
+        return self.image
+
diff --git a/vel/api/metrics/summing_metric.py b/vel/metric/base/summing_metric.py
similarity index 90%
rename from vel/api/metrics/summing_metric.py
rename to vel/metric/base/summing_metric.py
index 3d1a389e..f08ceab7 100644
--- a/vel/api/metrics/summing_metric.py
+++ b/vel/metric/base/summing_metric.py
@@ -3,8 +3,8 @@
 
 class SummingMetric(BaseMetric):
     """ Base class for metrics that simply calculate the sum over the epoch """
-    def __init__(self, name, reset_value=True):
-        super().__init__(name)
+    def __init__(self, name, scope="general", reset_value=True):
+        super().__init__(name, scope=scope)
 
         self.reset_value = reset_value
         self.buffer = 0
diff --git a/vel/api/metrics/value_metric.py b/vel/metric/base/value_metric.py
similarity index 87%
rename from vel/api/metrics/value_metric.py
rename to vel/metric/base/value_metric.py
index ac17adbf..934ecc0c 100644
--- a/vel/api/metrics/value_metric.py
+++ b/vel/metric/base/value_metric.py
@@ -4,8 +4,8 @@
 class ValueMetric(BaseMetric):
     """ Base class for metrics that don't have state and just calculate a simple value """
 
-    def __init__(self, name):
-        super().__init__(name)
+    def __init__(self, name, scope="general"):
+        super().__init__(name, scope=scope)
 
         self._metric_value = None
 
@@ -23,4 +23,3 @@ def value(self):
 
     def _value_function(self, batch_info):
         raise NotImplementedError
-
diff --git a/vel/metric/loss_metric.py b/vel/metric/loss_metric.py
new file mode 100644
index 00000000..b2f45ce6
--- /dev/null
+++ b/vel/metric/loss_metric.py
@@ -0,0 +1,11 @@
+from vel.metric.base.averaging_metric import AveragingMetric
+
+
+class Loss(AveragingMetric):
+    """ Just a loss function """
+    def __init__(self, scope="train"):
+        super().__init__("loss", scope=scope)
+
+    def _value_function(self, batch_info):
+        """ Just forward a value of the loss"""
+        return batch_info['loss']
diff --git a/vel/metric/samples_per_sec.py b/vel/metric/samples_per_sec.py
new file mode 100644
index 00000000..edac2291
--- /dev/null
+++ b/vel/metric/samples_per_sec.py
@@ -0,0 +1,11 @@
+from vel.metric.base.averaging_metric import AveragingMetric
+
+
+class SamplesPerSec(AveragingMetric):
+    """ Just a loss function """
+    def __init__(self, scope="train"):
+        super().__init__("samples_per_sec", scope=scope)
+
+    def _value_function(self, batch_info):
+        """ Just forward a value of the loss"""
+        return batch_info['samples'] / batch_info['time']
diff --git a/vel/metrics/loss_metric.py b/vel/metrics/loss_metric.py
deleted file mode 100644
index d241a393..00000000
--- a/vel/metrics/loss_metric.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from vel.api.metrics.averaging_metric import AveragingMetric
-
-
-class Loss(AveragingMetric):
-    """ Just a loss function """
-    def __init__(self):
-        super().__init__("loss")
-
-    def _value_function(self, batch_info):
-        """ Just forward a value of the loss"""
-        return batch_info['loss'].item()
diff --git a/vel/modules/__init__.py b/vel/model/__init__.py
similarity index 100%
rename from vel/modules/__init__.py
rename to vel/model/__init__.py
diff --git a/vel/modules/input/__init__.py b/vel/model/autoencoder/__init__.py
similarity index 100%
rename from vel/modules/input/__init__.py
rename to vel/model/autoencoder/__init__.py
diff --git a/vel/model/autoencoder/cnn_autoencoder.py b/vel/model/autoencoder/cnn_autoencoder.py
new file mode 100644
index 00000000..51ca0907
--- /dev/null
+++ b/vel/model/autoencoder/cnn_autoencoder.py
@@ -0,0 +1,107 @@
+import itertools as it
+
+import torch.nn as nn
+import torch.nn.init as init
+import torch.nn.functional as F
+
+import vel.util.network as net_util
+
+from vel.api import LossFunctionModel, ModuleFactory
+from vel.metric.loss_metric import Loss
+from vel.module.layers import Flatten, Reshape
+
+
+class MnistCnnAutoencoder(LossFunctionModel):
+    """
+    A simple MNIST autoencoder, containing 3 convolutional layers.
+    """
+
+    def __init__(self, img_rows, img_cols, img_channels, channels=None, representation_length=32):
+        super(MnistCnnAutoencoder, self).__init__()
+
+        if channels is None:
+            channels = [16, 32, 32]
+
+        layer_series = [
+            (3, 1, 1),
+            (3, 1, 2),
+            (3, 1, 2),
+        ]
+
+        self.representation_length = representation_length
+        self.final_width = net_util.convolutional_layer_series(img_rows, layer_series)
+        self.final_height = net_util.convolutional_layer_series(img_cols, layer_series)
+        self.channels = channels
+
+        self.encoder = nn.Sequential(
+            nn.Conv2d(in_channels=img_channels, out_channels=channels[0], kernel_size=(3, 3), padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(in_channels=channels[0], out_channels=channels[1], kernel_size=(3, 3), stride=2, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(in_channels=channels[1], out_channels=channels[2], kernel_size=(3, 3), stride=2, padding=1),
+            Flatten(),
+            nn.Linear(self.final_width * self.final_height * channels[2], representation_length)
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(representation_length, self.final_width * self.final_height * channels[2]),
+            nn.ReLU(True),
+            Reshape(channels[2], self.final_width, self.final_height),
+            nn.ConvTranspose2d(
+                in_channels=channels[2], out_channels=channels[1], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(
+                in_channels=channels[1], out_channels=channels[0], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            nn.ReLU(True),
+            nn.ConvTranspose2d(in_channels=channels[0], out_channels=img_channels, kernel_size=3, padding=1),
+            nn.Sigmoid()
+        )
+
+    @staticmethod
+    def _weight_initializer(tensor):
+        init.xavier_uniform_(tensor.weight, gain=init.calculate_gain('relu'))
+        init.constant_(tensor.bias, 0.0)
+
+    def reset_weights(self):
+        for m in it.chain(self.encoder, self.decoder):
+            if isinstance(m, nn.Conv2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.ConvTranspose2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.Linear):
+                self._weight_initializer(m)
+
+    def forward(self, x):
+        encoding = self.encoder(x)
+        decoded = self.decoder(encoding)
+        return decoded
+
+    def encode(self, sample):
+        return self.encoder(sample)
+
+    def decode(self, sample):
+        return self.decoder(sample)
+
+    def loss_value(self, x_data, y_true, y_pred):
+        """ Calculate a value of loss function """
+        # return F.mse_loss(y_pred, y_true)
+        return F.binary_cross_entropy(y_pred, y_true)
+
+    def metrics(self):
+        """ Set of metrics for this model """
+        return [Loss()]
+
+
+def create(img_rows, img_cols, img_channels, channels=None, representation_length=32):
+    """ Vel factory function """
+    if channels is None:
+        channels = [16, 32, 32]
+
+    def instantiate(**_):
+        return MnistCnnAutoencoder(
+            img_rows, img_cols, img_channels, channels=channels, representation_length=representation_length
+        )
+
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/optimizers/__init__.py b/vel/model/gan/__init__.py
similarity index 100%
rename from vel/optimizers/__init__.py
rename to vel/model/gan/__init__.py
diff --git a/vel/model/gan/simple_gan.py b/vel/model/gan/simple_gan.py
new file mode 100644
index 00000000..310c41d1
--- /dev/null
+++ b/vel/model/gan/simple_gan.py
@@ -0,0 +1,193 @@
+"""
+Simple GAN code is based on https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/gan/gan.py
+"""
+import collections
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from vel.api import OptimizedModel, ModuleFactory, OptimizerFactory
+from vel.api.optimizer import VelMultiOptimizer
+from vel.metric import AveragingNamedMetric, RandomImageMetric
+
+
+class Generator(nn.Module):
+    def __init__(self, img_shape, latent_dim):
+        super(Generator, self).__init__()
+
+        self.img_shape = img_shape
+        self.latent_dim = latent_dim
+
+        def block(in_feat, out_feat, normalize=True):
+            layers = [nn.Linear(in_feat, out_feat)]
+            if normalize:
+                layers.append(nn.BatchNorm1d(out_feat, 0.8))
+            layers.append(nn.LeakyReLU(0.2, inplace=True))
+            return layers
+
+        self.model = nn.Sequential(
+            *block(latent_dim, 128, normalize=False),
+            *block(128, 256),
+            *block(256, 512),
+            *block(512, 1024),
+            nn.Linear(1024, int(np.prod(img_shape))),
+            nn.Sigmoid()
+        )
+
+    def forward(self, z):
+        img = self.model(z)
+        img = img.view(img.size(0), *self.img_shape)
+        return img
+
+
+class Discriminator(nn.Module):
+    def __init__(self, img_shape, latent_dim):
+        super(Discriminator, self).__init__()
+
+        self.model = nn.Sequential(
+            # nn.Linear(int(np.prod(img_shape)), 512),
+            # nn.LeakyReLU(0.2, inplace=True),
+            # nn.Linear(512, 256),
+            # nn.LeakyReLU(0.2, inplace=True),
+            # nn.Linear(256, 1),
+            nn.Linear(int(np.prod(img_shape)), 256),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Dropout(0.2),
+            nn.Linear(256, 128),
+            nn.LeakyReLU(0.2, inplace=True),
+            nn.Dropout(0.2),
+            nn.Linear(128, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, img):
+        img_flat = img.view(img.size(0), -1)
+        validity = self.model(img_flat)
+
+        return validity
+
+
+class SimpleGAN(OptimizedModel):
+    """
+    Implements simple Generative Adversarial Network in the spirit of the original paper
+    "Generative Adversarial Networks" https://arxiv.org/abs/1406.2661
+    """
+
+    def __init__(self, img_rows, img_cols, img_channels, latent_dim):
+        super().__init__()
+
+        self.image_shape = (img_channels, img_rows, img_cols)
+        self.latent_dim = latent_dim
+
+        self.generator = Generator(img_shape=self.image_shape, latent_dim=self.latent_dim)
+        self.discriminator = Discriminator(img_shape=self.image_shape, latent_dim=self.latent_dim)
+
+        self.adversarial_loss = nn.BCELoss()
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelMultiOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        gen_parameters = filter(lambda p: p.requires_grad, self.generator.parameters())
+        disc_parameters = filter(lambda p: p.requires_grad, self.discriminator.parameters())
+
+        return optimizer_factory.instantiate_multi(collections.OrderedDict([
+            ('generator', gen_parameters),
+            ('discriminator', disc_parameters)
+        ]))
+
+    def optimize(self, data: dict, optimizer: VelMultiOptimizer) -> dict:
+        """
+        Perform one step of optimization of the model
+        :returns a dictionary of metrics
+        """
+        optimizer_G = optimizer['generator']
+        optimizer_D = optimizer['discriminator']
+
+        input_data = data['x']
+
+        # Adversarial ground truths
+        valid = torch.ones(input_data.size(0), 1).to(input_data.device)
+        fake = torch.zeros(input_data.size(0), 1).to(input_data.device)
+
+        optimizer_G.zero_grad()
+
+        # Sample noise as generator input
+        z = torch.randn(input_data.size(0), self.latent_dim).to(input_data.device)
+
+        # Generate a batch of images
+        gen_imgs = self.generator(z)
+
+        # Loss measures generator's ability to fool the discriminator
+        g_loss = self.adversarial_loss(self.discriminator(gen_imgs), valid)
+
+        g_loss.backward()
+        g_metrics = optimizer_G.step()
+
+        # ---------------------
+        #  Train Discriminator
+        # ---------------------
+
+        optimizer_D.zero_grad()
+
+        # Measure discriminator's ability to classify real from generated samples
+        input_data_prob = self.discriminator(input_data)
+        generated_images_prob = self.discriminator(gen_imgs.detach())
+
+        real_loss = self.adversarial_loss(input_data_prob, valid)
+        fake_loss = self.adversarial_loss(generated_images_prob, fake)
+
+        d_loss = (real_loss + fake_loss) / 2
+
+        d_loss.backward()
+        d_metrics = optimizer_D.step()
+
+        optimizer_metrics = optimizer.aggregate_metrics({
+            'generator': g_metrics,
+            'discriminator': d_metrics
+        })
+
+        # Log images to see how we're doing
+        np_image = gen_imgs[0].detach().cpu().numpy()
+        np_image = np.transpose(np_image, (2, 1, 0))
+
+        return {
+            **optimizer_metrics,
+            'gen_loss': g_loss.item(),
+            'disc_loss': d_loss.item(),
+            'discriminator_real_accuracy': (input_data_prob > 0.5).float().mean().item(),
+            'discriminator_fake_accuracy': (generated_images_prob < 0.5).float().mean().item(),
+            'generated_image': np_image
+        }
+
+    def validate(self, data: dict) -> dict:
+        """
+        Perform one step of model inference without optimization
+        :returns a dictionary of metrics
+        """
+        return {
+            'gen_loss': 0.0,
+            'disc_loss': 0.0,
+            'discriminator_real_accuracy': 0.0,
+            'discriminator_fake_accuracy': 0.0,
+            'generated_image': None
+        }
+
+    def metrics(self):
+        """ Set of metrics for this model """
+        return [
+            AveragingNamedMetric('gen_loss', scope="train"),
+            AveragingNamedMetric('disc_loss', scope="train"),
+            AveragingNamedMetric('discriminator_real_accuracy', scope="train"),
+            AveragingNamedMetric('discriminator_fake_accuracy', scope="train"),
+            RandomImageMetric('generated_image', scope='train')
+        ]
+
+
+def create(img_rows, img_cols, img_channels, latent_dim):
+    """ Vel factory function """
+    def instantiate(**_):
+        return SimpleGAN(
+            img_rows, img_cols, img_channels, latent_dim=latent_dim
+        )
+
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/phase/__init__.py b/vel/model/imagenet/__init__.py
similarity index 100%
rename from vel/phase/__init__.py
rename to vel/model/imagenet/__init__.py
diff --git a/vel/models/imagenet/resnet34.py b/vel/model/imagenet/resnet34.py
similarity index 71%
rename from vel/models/imagenet/resnet34.py
rename to vel/model/imagenet/resnet34.py
index e5a5a97c..6139a44d 100644
--- a/vel/models/imagenet/resnet34.py
+++ b/vel/model/imagenet/resnet34.py
@@ -2,17 +2,17 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-import vel.modules.layers as l
+import vel.module.layers as layers
 import vel.util.module_util as mu
 
-from vel.api import SupervisedModel, ModelFactory
+from vel.api import LossFunctionModel, ModuleFactory, OptimizerFactory, VelOptimizer
 
 
 # Because of concat pooling it's 2x 512
 NET_OUTPUT = 1024
 
 
-class Resnet34(SupervisedModel):
+class Resnet34(LossFunctionModel):
     """ Resnet34 network model """
 
     def __init__(self, fc_layers=None, dropout=None, pretrained=True):
@@ -35,8 +35,8 @@ def __init__(self, fc_layers=None, dropout=None, pretrained=True):
             valid_children = list(backbone.children())[:-2]
 
             valid_children.extend([
-                l.AdaptiveConcatPool2d(),
-                l.Flatten()
+                layers.AdaptiveConcatPool2d(),
+                layers.Flatten()
             ])
 
             layer_inputs = [NET_OUTPUT] + fc_layers[:-1]
@@ -63,26 +63,39 @@ def __init__(self, fc_layers=None, dropout=None, pretrained=True):
 
         self.model = final_model
 
-    def freeze(self, number=None):
+    def freeze(self, groups=None):
         """ Freeze given number of layers in the model """
-        if number is None:
-            number = self.head_layers
+        layer_groups = dict(self.layer_groups())
 
-        for idx, child in enumerate(self.model.children()):
-            if idx < number:
-                mu.freeze_layer(child)
+        if groups is None:
+            groups = layer_groups.keys()
+
+        for group in groups:
+            for module in layer_groups[group]:
+                mu.freeze_layer(module)
 
     def unfreeze(self):
         """ Unfreeze model layers """
         for idx, child in enumerate(self.model.children()):
             mu.unfreeze_layer(child)
 
-    def get_layer_groups(self):
+    def layer_groups(self):
         """ Return layers grouped """
         g1 = list(self.model[:self.group_cut_layers[0]])
         g2 = list(self.model[self.group_cut_layers[0]:self.group_cut_layers[1]])
         g3 = list(self.model[self.group_cut_layers[1]:])
-        return [g1, g2, g3]
+
+        return [
+            ('top', g1),
+            ('mid', g2),
+            ('bottom', g3)
+        ]
+
+    def parameter_groups(self):
+        return [(name, mu.module_list_to_param_list(m)) for name, m in self.layer_groups()]
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        return optimizer_factory.instantiate(self.parameter_groups())
 
     def forward(self, x):
         """ Calculate model value """
@@ -94,8 +107,8 @@ def loss_value(self, x_data, y_true, y_pred):
 
     def metrics(self):
         """ Set of metrics for this model """
-        from vel.metrics.loss_metric import Loss
-        from vel.metrics.accuracy import Accuracy
+        from vel.metric.loss_metric import Loss
+        from vel.metric.accuracy import Accuracy
         return [Loss(), Accuracy()]
 
 
@@ -104,4 +117,4 @@ def create(fc_layers=None, dropout=None, pretrained=True):
     def instantiate(**_):
         return Resnet34(fc_layers, dropout, pretrained)
 
-    return ModelFactory.generic(instantiate)
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/model/latent/cnn_iwae.py b/vel/model/latent/cnn_iwae.py
new file mode 100644
index 00000000..6827b0af
--- /dev/null
+++ b/vel/model/latent/cnn_iwae.py
@@ -0,0 +1,158 @@
+import itertools as it
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+import torch.nn.functional as F
+import torch.distributions as dist
+
+import vel.util.network as net_util
+
+from vel.api import ModuleFactory
+from vel.module.layers import Flatten, Reshape
+from vel.model.latent.iwae import IWAE
+
+
+class CnnIWAE(IWAE):
+    """
+    A simple IWAE, containing 3 convolutional layers
+    """
+
+    def __init__(self, img_rows, img_cols, img_channels, k=5, channels=None, representation_length=32,
+                 analytical_kl_div=True):
+        super().__init__(k=k, analytical_kl_div=analytical_kl_div)
+
+        if channels is None:
+            channels = [16, 32, 32]
+
+        layer_series = [
+            (3, 1, 1),
+            (3, 1, 2),
+            (3, 1, 2),
+        ]
+
+        self.representation_length = representation_length
+
+        self.final_width = net_util.convolutional_layer_series(img_rows, layer_series)
+        self.final_height = net_util.convolutional_layer_series(img_cols, layer_series)
+        self.channels = channels
+
+        self.encoder = nn.Sequential(
+            nn.Conv2d(in_channels=img_channels, out_channels=channels[0], kernel_size=(3, 3), padding=1),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[0],
+                net_util.convolutional_layer_series(img_rows, layer_series[:1]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:1]),
+            ]),
+            nn.Conv2d(in_channels=channels[0], out_channels=channels[1], kernel_size=(3, 3), stride=2, padding=1),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[1],
+                net_util.convolutional_layer_series(img_rows, layer_series[:2]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:2]),
+            ]),
+            nn.Conv2d(in_channels=channels[1], out_channels=channels[2], kernel_size=(3, 3), stride=2, padding=1),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[2],
+                net_util.convolutional_layer_series(img_rows, layer_series),
+                net_util.convolutional_layer_series(img_cols, layer_series),
+            ]),
+            Flatten(),
+            nn.Linear(self.final_width * self.final_height * channels[2], representation_length * 2)
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(representation_length, self.final_width * self.final_height * channels[2]),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            Reshape(channels[2], self.final_width, self.final_height),
+            nn.LayerNorm([
+                channels[2],
+                net_util.convolutional_layer_series(img_rows, layer_series),
+                net_util.convolutional_layer_series(img_cols, layer_series),
+            ]),
+            nn.ConvTranspose2d(
+                in_channels=channels[2], out_channels=channels[1], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[1],
+                net_util.convolutional_layer_series(img_rows, layer_series[:2]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:2]),
+            ]),
+            nn.ConvTranspose2d(
+                in_channels=channels[1], out_channels=channels[0], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[0],
+                net_util.convolutional_layer_series(img_rows, layer_series[:1]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:1]),
+            ]),
+            nn.ConvTranspose2d(in_channels=channels[0], out_channels=img_channels, kernel_size=3, padding=1),
+            nn.Sigmoid()
+        )
+
+        self.register_buffer('prior_mean', torch.tensor([[0.0]]))
+        self.register_buffer('prior_std', torch.tensor([[1.0]]))
+
+    @staticmethod
+    def _weight_initializer(tensor):
+        init.xavier_uniform_(tensor.weight, gain=init.calculate_gain('relu'))
+        init.constant_(tensor.bias, 0.0)
+
+    def reset_weights(self):
+        for m in it.chain(self.encoder, self.decoder):
+            if isinstance(m, nn.Conv2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.ConvTranspose2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.Linear):
+                self._weight_initializer(m)
+
+    def encoder_network(self, sample: torch.Tensor) -> torch.Tensor:
+        """ Transform input sample into an encoded representation """
+        return self.encoder(sample)
+
+    def encoder_distribution(self, encoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the encoder distribution (approximate posterior) """
+        mu = encoded[:, :self.representation_length]
+        std = F.softplus(encoded[:, self.representation_length:])
+
+        return dist.Independent(dist.Normal(mu, std), 1)
+
+    def decoder_network(self, z: torch.Tensor) -> torch.Tensor:
+        """ Transform encoded value into a decoded representation """
+        return self.decoder(z)
+
+    def decoder_distribution(self, decoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the decoder distribution (likelihood) """
+        return dist.Independent(dist.Bernoulli(probs=decoded), 3)
+
+    def prior_distribution(self) -> dist.Distribution:
+        """ Return a prior distribution object """
+        return dist.Independent(dist.Normal(self.prior_mean, self.prior_std), 1)
+
+    def decoder_sample(self, decoded: torch.Tensor) -> torch.Tensor:
+        """ Sample from a decoder distribution - we ignore that since it's so weak in this case """
+        return decoded
+
+
+def create(img_rows, img_cols, img_channels, k=5, channels=None, representation_length=32):
+    """ Vel factory function """
+    if channels is None:
+        channels = [16, 32, 32]
+
+    def instantiate(**_):
+        return CnnIWAE(
+            img_rows, img_cols, img_channels, k=k, channels=channels, representation_length=representation_length
+        )
+
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/model/latent/cnn_vae.py b/vel/model/latent/cnn_vae.py
new file mode 100644
index 00000000..71582449
--- /dev/null
+++ b/vel/model/latent/cnn_vae.py
@@ -0,0 +1,158 @@
+import itertools as it
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+import torch.nn.functional as F
+import torch.distributions as dist
+
+import vel.util.network as net_util
+
+from vel.api import ModuleFactory
+from vel.module.layers import Flatten, Reshape
+from vel.model.latent.vae_base import VaeBase
+
+
+class CnnVAE(VaeBase):
+    """
+    A simple variational autoencoder, containing 3 convolutional layers.
+    """
+
+    def __init__(self, img_rows, img_cols, img_channels, channels=None, representation_length=32,
+                 analytical_kl_div=True):
+        super().__init__(analytical_kl_div=analytical_kl_div)
+
+        if channels is None:
+            channels = [16, 32, 32]
+
+        layer_series = [
+            (3, 1, 1),
+            (3, 1, 2),
+            (3, 1, 2),
+        ]
+
+        self.representation_length = representation_length
+
+        self.final_width = net_util.convolutional_layer_series(img_rows, layer_series)
+        self.final_height = net_util.convolutional_layer_series(img_cols, layer_series)
+        self.channels = channels
+
+        self.encoder = nn.Sequential(
+            nn.Conv2d(in_channels=img_channels, out_channels=channels[0], kernel_size=(3, 3), padding=1),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[0],
+                net_util.convolutional_layer_series(img_rows, layer_series[:1]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:1]),
+            ]),
+            nn.Conv2d(in_channels=channels[0], out_channels=channels[1], kernel_size=(3, 3), stride=2, padding=1),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[1],
+                net_util.convolutional_layer_series(img_rows, layer_series[:2]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:2]),
+            ]),
+            nn.Conv2d(in_channels=channels[1], out_channels=channels[2], kernel_size=(3, 3), stride=2, padding=1),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[2],
+                net_util.convolutional_layer_series(img_rows, layer_series),
+                net_util.convolutional_layer_series(img_cols, layer_series),
+            ]),
+            Flatten(),
+            nn.Linear(self.final_width * self.final_height * channels[2], representation_length * 2)
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(representation_length, self.final_width * self.final_height * channels[2]),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            Reshape(channels[2], self.final_width, self.final_height),
+            nn.LayerNorm([
+                channels[2],
+                net_util.convolutional_layer_series(img_rows, layer_series),
+                net_util.convolutional_layer_series(img_cols, layer_series),
+            ]),
+            nn.ConvTranspose2d(
+                in_channels=channels[2], out_channels=channels[1], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[1],
+                net_util.convolutional_layer_series(img_rows, layer_series[:2]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:2]),
+            ]),
+            nn.ConvTranspose2d(
+                in_channels=channels[1], out_channels=channels[0], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[0],
+                net_util.convolutional_layer_series(img_rows, layer_series[:1]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:1]),
+            ]),
+            nn.ConvTranspose2d(in_channels=channels[0], out_channels=img_channels, kernel_size=3, padding=1),
+            nn.Sigmoid()
+        )
+
+        self.register_buffer('prior_mean', torch.tensor([[0.0]]))
+        self.register_buffer('prior_std', torch.tensor([[1.0]]))
+
+    @staticmethod
+    def _weight_initializer(tensor):
+        init.xavier_uniform_(tensor.weight, gain=init.calculate_gain('relu'))
+        init.constant_(tensor.bias, 0.0)
+
+    def reset_weights(self):
+        for m in it.chain(self.encoder, self.decoder):
+            if isinstance(m, nn.Conv2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.ConvTranspose2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.Linear):
+                self._weight_initializer(m)
+
+    def encoder_network(self, sample: torch.Tensor) -> torch.Tensor:
+        """ Transform input sample into an encoded representation """
+        return self.encoder(sample)
+
+    def encoder_distribution(self, encoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the encoder distribution (approximate posterior) """
+        mu = encoded[:, :self.representation_length]
+        std = F.softplus(encoded[:, self.representation_length:])
+
+        return dist.Independent(dist.Normal(mu, std), 1)
+
+    def decoder_network(self, z: torch.Tensor) -> torch.Tensor:
+        """ Transform encoded value into a decoded representation """
+        return self.decoder(z)
+
+    def decoder_distribution(self, decoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the decoder distribution (likelihood) """
+        return dist.Independent(dist.Bernoulli(probs=decoded), 3)
+
+    def prior_distribution(self) -> dist.Distribution:
+        """ Return a prior distribution object """
+        return dist.Independent(dist.Normal(self.prior_mean, self.prior_std), 1)
+
+    def decoder_sample(self, decoded: torch.Tensor) -> torch.Tensor:
+        """ Sample from a decoder distribution - we ignore that since it's so weak in this case """
+        return decoded
+
+
+def create(img_rows, img_cols, img_channels, channels=None, representation_length=32):
+    """ Vel factory function """
+    if channels is None:
+        channels = [16, 32, 32]
+
+    def instantiate(**_):
+        return CnnVAE(
+            img_rows, img_cols, img_channels, channels=channels, representation_length=representation_length
+        )
+
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/model/latent/fc_iwae.py b/vel/model/latent/fc_iwae.py
new file mode 100644
index 00000000..4ba19597
--- /dev/null
+++ b/vel/model/latent/fc_iwae.py
@@ -0,0 +1,105 @@
+import torch
+import torch.distributions as dist
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vel.api import ModuleFactory
+from vel.module.layers import Flatten, Reshape
+
+from vel.model.latent.iwae import IWAE
+
+
+class FcIwae(IWAE):
+    """
+    A simple IWAE, containing 2 fully connected layers.
+    """
+
+    def __init__(self, img_rows, img_cols, img_channels, k=5, layers=None, representation_length=32,
+                 analytical_kl_div=False):
+        super().__init__(k=k, analytical_kl_div=analytical_kl_div)
+
+        if layers is None:
+            layers = [512, 256]
+
+        self.representation_length = representation_length
+        self.layers = layers
+
+        input_length = img_rows * img_cols * img_channels
+
+        self.encoder = nn.Sequential(
+            Flatten(),
+            nn.Linear(in_features=input_length, out_features=self.layers[0]),
+            nn.Tanh(),
+            nn.Linear(in_features=self.layers[0], out_features=self.layers[1]),
+            nn.Tanh(),
+            nn.Linear(self.layers[1], representation_length * 2)
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(in_features=representation_length, out_features=self.layers[1]),
+            nn.Tanh(),
+            nn.Linear(in_features=self.layers[1], out_features=self.layers[0]),
+            nn.Tanh(),
+            nn.Linear(in_features=self.layers[0], out_features=input_length),
+            Reshape(img_channels, img_rows, img_cols),
+            nn.Sigmoid()
+        )
+
+        self.register_buffer('prior_mean', torch.tensor([[0.0]]))
+        self.register_buffer('prior_std', torch.tensor([[1.0]]))
+
+    def encoder_network(self, sample: torch.Tensor) -> torch.Tensor:
+        """ Transform input sample into an encoded representation """
+        return self.encoder(sample)
+
+    def encoder_distribution(self, encoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the encoder distribution (approximate posterior) """
+        mu = encoded[:, :self.representation_length]
+        std = F.softplus(encoded[:, self.representation_length:])
+
+        return dist.Independent(dist.Normal(mu, std), 1)
+
+    def decoder_network(self, z: torch.Tensor) -> torch.Tensor:
+        """ Transform encoded value into a decoded representation """
+        return self.decoder(z)
+
+    def decoder_distribution(self, decoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the decoder distribution (likelihood) """
+        return dist.Independent(dist.Bernoulli(probs=decoded), 3)
+
+    def prior_distribution(self) -> dist.Distribution:
+        """ Return a prior distribution object """
+        return dist.Independent(dist.Normal(self.prior_mean, self.prior_std), 1)
+
+    def decoder_sample(self, decoded: torch.Tensor) -> torch.Tensor:
+        """ Sample from a decoder distribution - we ignore that since it's so weak in this case """
+        return decoded
+
+# import torch.nn.init as init
+#     @staticmethod
+#     def _weight_initializer(tensor):
+#         init.xavier_uniform_(tensor.weight, gain=init.calculate_gain('tanh'))
+#         init.constant_(tensor.bias, 0.01)
+#
+#     def reset_weights(self):
+#         for m in it.chain(self.encoder, self.decoder):
+#             if isinstance(m, nn.Conv2d):
+#                 self._weight_initializer(m)
+#             elif isinstance(m, nn.ConvTranspose2d):
+#                 self._weight_initializer(m)
+#             elif isinstance(m, nn.Linear):
+#                 self._weight_initializer(m)
+
+
+def create(img_rows, img_cols, img_channels, k=5, layers=None, representation_length=32, analytical_kl_div=True):
+    """ Vel factory function """
+    if layers is None:
+        layers = [512, 256]
+
+    def instantiate(**_):
+        return FcIwae(
+            img_rows, img_cols, img_channels, k=k, layers=layers, representation_length=representation_length,
+            analytical_kl_div=analytical_kl_div
+        )
+
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/model/latent/fc_vae.py b/vel/model/latent/fc_vae.py
new file mode 100644
index 00000000..739443d8
--- /dev/null
+++ b/vel/model/latent/fc_vae.py
@@ -0,0 +1,108 @@
+import itertools as it
+
+import torch
+import torch.distributions as dist
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+
+from vel.api import ModuleFactory
+from vel.module.layers import Flatten, Reshape
+
+from vel.model.latent.vae_base import VaeBase
+
+
+class FcVae(VaeBase):
+    """
+    A simple variational autoencoder, containing 2 fully connected layers.
+    """
+
+    def __init__(self, img_rows, img_cols, img_channels, layers=None, representation_length=32,
+                 analytical_kl_div=False):
+        super().__init__(analytical_kl_div=analytical_kl_div)
+
+        if layers is None:
+            layers = [512, 256]
+
+        self.representation_length = representation_length
+        self.layers = layers
+
+        input_length = img_rows * img_cols * img_channels
+
+        self.encoder = nn.Sequential(
+            Flatten(),
+            nn.Linear(in_features=input_length, out_features=self.layers[0]),
+            nn.Tanh(),
+            nn.Linear(in_features=self.layers[0], out_features=self.layers[1]),
+            nn.Tanh(),
+            nn.Linear(self.layers[1], representation_length * 2)
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(in_features=representation_length, out_features=self.layers[1]),
+            nn.Tanh(),
+            nn.Linear(in_features=self.layers[1], out_features=self.layers[0]),
+            nn.Tanh(),
+            nn.Linear(in_features=self.layers[0], out_features=input_length),
+            Reshape(img_channels, img_rows, img_cols),
+            nn.Sigmoid()
+        )
+
+        self.register_buffer('prior_mean', torch.tensor([[0.0]]))
+        self.register_buffer('prior_std', torch.tensor([[1.0]]))
+
+    def encoder_network(self, sample: torch.Tensor) -> torch.Tensor:
+        """ Transform input sample into an encoded representation """
+        return self.encoder(sample)
+
+    def encoder_distribution(self, encoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the encoder distribution (approximate posterior) """
+        mu = encoded[:, :self.representation_length]
+        std = F.softplus(encoded[:, self.representation_length:])
+
+        return dist.Independent(dist.Normal(mu, std), 1)
+
+    def decoder_network(self, z: torch.Tensor) -> torch.Tensor:
+        """ Transform encoded value into a decoded representation """
+        return self.decoder(z)
+
+    def decoder_distribution(self, decoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the decoder distribution (likelihood) """
+        return dist.Independent(dist.Bernoulli(probs=decoded), 3)
+
+    def prior_distribution(self) -> dist.Distribution:
+        """ Return a prior distribution object """
+        return dist.Independent(dist.Normal(self.prior_mean, self.prior_std), 1)
+
+    def decoder_sample(self, decoded: torch.Tensor) -> torch.Tensor:
+        """ Sample from a decoder distribution - we ignore that since it's so weak in this case """
+        return decoded
+
+    # @staticmethod
+    # def _weight_initializer(tensor):
+    #     init.xavier_normal_(tensor.weight, gain=init.calculate_gain('tanh'))
+    #     init.zeros_(tensor.bias)
+    #
+    # def reset_weights(self):
+    #     for m in it.chain(self.encoder.modules(), self.decoder.modules()):
+    #         if isinstance(m, nn.Conv2d):
+    #             self._weight_initializer(m)
+    #         elif isinstance(m, nn.ConvTranspose2d):
+    #             self._weight_initializer(m)
+    #         elif isinstance(m, nn.Linear):
+    #             self._weight_initializer(m)
+
+
+def create(img_rows, img_cols, img_channels, layers=None, representation_length=32,
+           analytical_kl_div=True):
+    """ Vel factory function """
+    if layers is None:
+        layers = [512, 256]
+
+    def instantiate(**_):
+        return FcVae(
+            img_rows, img_cols, img_channels, layers=layers, representation_length=representation_length,
+            analytical_kl_div=analytical_kl_div
+        )
+
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/model/latent/iwae.py b/vel/model/latent/iwae.py
new file mode 100644
index 00000000..faa24a68
--- /dev/null
+++ b/vel/model/latent/iwae.py
@@ -0,0 +1,61 @@
+import torch.nn.utils
+
+from vel.model.latent.vae_base import VaeBase
+
+
+class IWAE(VaeBase):
+    """
+    Importance-Weighted Auto-Encoder https://arxiv.org/abs/1509.00519
+    """
+
+    def __init__(self, k: int = 5, analytical_kl_div=True):
+        super().__init__(analytical_kl_div=analytical_kl_div)
+
+        self.k = k
+
+    def calculate_gradient(self, data: dict) -> dict:
+        """ Calculate model gradient for given data sample """
+        encoded = self.encoder_network(data['x'])
+        z_dist = self.encoder_distribution(encoded)
+
+        bs = encoded.size(0)
+        # Encode importance samples into batch dimension for the decoded network
+        z = z_dist.rsample([self.k]).reshape([bs * self.k, -1])
+
+        decoded = self.decoder_network(z)
+        decoded = decoded.reshape([self.k, bs] + list(decoded.shape[1:]))
+
+        # Unpack to make distribution efficient for broadcasting
+        x_dist = self.decoder_distribution(decoded)
+        prior = self.prior_distribution()
+
+        kl_divergence = self.kl_divergence(z, z_dist, prior)
+        reconstruction = x_dist.log_prob(data['y'])
+
+        # ELBO is E_q log p(x, z) / q(z | x)
+        # Which can be expressed in many equivalent forms:
+        # (1) E_q log p(x | z) + log p(z) - log q(z | x)
+        # (2) E_q log p(x | z) - D_KL(p(z) || q(z | x))
+        # (3) E_q log p(x) - D_KL(p(z | x) || q(z | x)Biblio)
+
+        # Form 3 is interesting from a theoretical standpoint, but is intractable to compute directly
+        # While forms (1) and (2) can be computed directly.
+        # Positive aspect of form (2) is that KL divergence can be calculated analytically
+        # further reducing the variance of the gradient
+        elbo = reconstruction - kl_divergence
+
+        # Perform log-mean-exp on the axis of importance samples
+        # Then mean across batch
+        elbo = self.log_mean_exp(elbo, 0).mean()
+
+        loss = -elbo
+
+        if self.training:
+            loss.backward()
+
+        with torch.no_grad():
+            return {
+                'loss': loss.item(),
+                'reconstruction': -reconstruction.mean().item(),
+                'kl_divergence': kl_divergence.mean().item()
+            }
diff --git a/vel/model/latent/vae_base.py b/vel/model/latent/vae_base.py
new file mode 100644
index 00000000..be480e50
--- /dev/null
+++ b/vel/model/latent/vae_base.py
@@ -0,0 +1,164 @@
+import torch
+import torch.distributions as dist
+
+from vel.api import GradientModel
+from vel.metric import AveragingNamedMetric
+from vel.metric.loss_metric import Loss
+
+
+class VaeBase(GradientModel):
+    """ Base module for variational auto-encoder implementations """
+
+    def __init__(self, analytical_kl_div=True):
+        super().__init__()
+
+        self.analytical_kl_div = analytical_kl_div
+
+    ####################################################################################################################
+    # Interface methods
+    def encoder_network(self, sample: torch.Tensor) -> torch.Tensor:
+        """ Transform input sample into an encoded representation """
+        raise NotImplementedError
+
+    def encoder_distribution(self, encoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the encoder distribution (approximate posterior) """
+        raise NotImplementedError
+
+    def decoder_network(self, z: torch.Tensor) -> torch.Tensor:
+        """ Transform encoded value into a decoded representation """
+        raise NotImplementedError
+
+    def decoder_distribution(self, decoded: torch.Tensor) -> dist.Distribution:
+        """ Create a pytorch distribution object representing the decoder distribution (likelihood) """
+        raise NotImplementedError
+
+    def prior_distribution(self) -> dist.Distribution:
+        """ Return a prior distribution object """
+        raise NotImplementedError
+
+    ####################################################################################################################
+    # Other useful methods
+    def encoder_rsample(self, encoded: torch.Tensor) -> torch.Tensor:
+        """ Sample with "reparametrization trick" encoder sample """
+        return self.encoder_distribution(encoded).rsample()
+
+    def decoder_sample(self, decoded: torch.Tensor) -> torch.Tensor:
+        """ Sample from a decoder distribution """
+        return self.decoder_distribution(decoded).sample()
+
+    def encode(self, sample: torch.Tensor) -> torch.Tensor:
+        """ Encode incoming data into a latent representation """
+        encoded = self.encoder_network(sample)
+        return self.encoder_rsample(encoded)
+
+    def decode(self, z: torch.Tensor) -> torch.Tensor:
+        """
+        Decode latent representation back into data domain.
+        Sample from p(x | z)
+        """
+        decoded = self.decoder_network(z)
+        return self.decoder_sample(decoded)
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        """ Simple forward pass through the module """
+        z = self.encode(sample)
+        decoded = self.decode(z)
+        return decoded
+
+    def calculate_gradient(self, data: dict) -> dict:
+        """ Calculate model gradient for given data sample """
+        encoded = self.encoder_network(data['x'])
+        z_dist = self.encoder_distribution(encoded)
+        z = z_dist.rsample()
+
+        decoded = self.decoder_network(z)
+        x_dist = self.decoder_distribution(decoded)
+        prior = self.prior_distribution()
+
+        kl_divergence = self.kl_divergence(z, z_dist, prior).mean()
+        reconstruction = x_dist.log_prob(data['y']).mean()
+
+        # ELBO is E_q log p(x, z) / q(z | x)
+        # Which can be expressed in many equivalent forms:
+        # (1) E_q log p(x | z) + log p(z) - log q(z | x)
+        # (2) E_q log p(x | z) - D_KL(p(z) || q(z | x))
+        # (3) E_q log p(x) - D_KL(p(z | x) || q(z | x)Biblio)
+
+        # Form 3 is interesting from a theoretical standpoint, but is intractable to compute directly
+        # While forms (1) and (2) can be computed directly.
+        # Positive aspect of form (2) is that KL divergence can be calculated analytically
+        # further reducing the variance of the gradient
+        elbo = reconstruction - kl_divergence
+
+        loss = -elbo
+
+        if self.training:
+            loss.backward()
+
+        return {
+            'loss': loss.item(),
+            'reconstruction': -reconstruction.item(),
+            'kl_divergence': kl_divergence.item()
+        }
+
+    def kl_divergence(self, z, encoder_distribution, prior) -> torch.Tensor:
+        """ Calculate the kl divergence between q(z|x) and p(z) """
+        if self.analytical_kl_div:
+            kl_divergence = dist.kl_divergence(encoder_distribution, prior)
+        else:
+            lpz = prior.log_prob(z)
+            lqzx = encoder_distribution.log_prob(z)
+            kl_divergence = -lpz + lqzx
+
+        return kl_divergence
+
+    def metrics(self):
+        """ Set of metrics for this model """
+        return [
+            Loss(),
+            AveragingNamedMetric('reconstruction', scope="train"),
+            AveragingNamedMetric('kl_divergence', scope="train"),
+        ]
+
+    @torch.no_grad()
+    def nll(self, sample: torch.Tensor, num_posterior_samples: int = 1):
+        """
+        Upper bound on negative log-likelihood of supplied data.
+        If num samples goes to infinity, the nll of data should
+        approach true value
+        """
+        assert num_posterior_samples >= 1, "Need at least one posterior sample"
+
+        encoded = self.encoder_network(sample)
+        z_dist = self.encoder_distribution(encoded)
+        prior = self.prior_distribution()
+
+        if self.analytical_kl_div:
+            kl_divergence = dist.kl_divergence(z_dist, prior)
+
+        bs = encoded.size(0)
+        z = z_dist.rsample((num_posterior_samples,))
+
+        # Reshape, decode, reshape
+        z_reshaped = z.view([bs * num_posterior_samples] + list(z.shape[2:]))
+        decoded = self.decoder_network(z_reshaped)
+        decoded = decoded.view([num_posterior_samples, bs] + list(decoded.shape[1:]))
+
+        x_dist = self.decoder_distribution(decoded)
+
+        if not self.analytical_kl_div:
+            lpz = prior.log_prob(z)
+            lqzx = z_dist.log_prob(z)
+            kl_divergence = -lpz + lqzx
+
+        likelihood = x_dist.log_prob(sample)
+        elbo = likelihood - kl_divergence
+
+        return -self.log_mean_exp(elbo, dim=0)
+
+    ####################################################################################################################
+    # Utility methods
+    def log_mean_exp(self, inputs, dim=1):
+        """ Perform log(mean(exp(data))) in a numerically stable way """
+        input_max = inputs.max(dim, keepdim=True)[0]
+        return (inputs - input_max).exp().mean(dim).log() + input_max.squeeze(dim=dim)
diff --git a/vel/model/latent/vq_vae.py b/vel/model/latent/vq_vae.py
new file mode 100644
index 00000000..608260f9
--- /dev/null
+++ b/vel/model/latent/vq_vae.py
@@ -0,0 +1,304 @@
+"""
+VQ-VAE implementation with Vector Quantization functions taken from
+https://github.com/ritheshkumar95/pytorch-vqvae/blob/master/functions.py
+"""
+import itertools as it
+
+import torch
+import torch.autograd as autograd
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+
+import vel.util.network as net_util
+
+from vel.api import GradientModel
+from vel.metric import AveragingNamedMetric
+from vel.metric.loss_metric import Loss
+
+
+class VectorQuantization(autograd.Function):
+    @staticmethod
+    def forward(ctx, inputs, codebook):
+        with torch.no_grad():
+            embedding_size = codebook.size(1)
+            inputs_size = inputs.size()
+            inputs_flatten = inputs.view(-1, embedding_size)
+
+            codebook_sqr = torch.sum(codebook ** 2, dim=1)
+            inputs_sqr = torch.sum(inputs_flatten ** 2, dim=1, keepdim=True)
+
+            # Compute the distances to the codebook
+            distances = torch.addmm(codebook_sqr + inputs_sqr, inputs_flatten, codebook.t(), alpha=-2.0, beta=1.0)
+
+            _, indices_flatten = torch.min(distances, dim=1)
+            indices = indices_flatten.view(*inputs_size[:-1])
+            ctx.mark_non_differentiable(indices)
+
+            return indices
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        raise RuntimeError(
+            'Trying to call `.grad()` on graph containing '
+            '`VectorQuantization`. The function `VectorQuantization` '
+            'is not differentiable. Use `VectorQuantizationStraightThrough` '
+            'if you want a straight-through estimator of the gradient.'
+        )
+
+
+class VectorQuantizationStraightThrough(autograd.Function):
+    @staticmethod
+    def forward(ctx, inputs, codebook):
+        indices = vector_quantization(inputs, codebook)
+        indices_flatten = indices.view(-1)
+        ctx.save_for_backward(indices_flatten, codebook)
+        ctx.mark_non_differentiable(indices_flatten)
+
+        codes_flatten = torch.index_select(codebook, dim=0, index=indices_flatten)
+        codes = codes_flatten.view_as(inputs)
+
+        return codes, indices_flatten
+
+    @staticmethod
+    def backward(ctx, grad_output, grad_indices):
+        grad_inputs, grad_codebook = None, None
+
+        if ctx.needs_input_grad[0]:
+            # Straight-through estimator
+            grad_inputs = grad_output.clone()
+
+        if ctx.needs_input_grad[1]:
+            # Gradient wrt. the codebook
+            indices, codebook = ctx.saved_tensors
+            embedding_size = codebook.size(1)
+
+            grad_output_flatten = (grad_output.contiguous().view(-1, embedding_size))
+            grad_codebook = torch.zeros_like(codebook)
+            grad_codebook.index_add_(0, indices, grad_output_flatten)
+
+        return grad_inputs, grad_codebook
+
+
+vector_quantization = VectorQuantization.apply
+vector_quantization_straight_through = VectorQuantizationStraightThrough.apply
+
+
+class VQEmbedding(nn.Module):
+    """ Vector-Quantised code embedding for the latent variables """
+
+    def __init__(self, k: int, d: int):
+        super().__init__()
+        self.k = k
+        self.d = d
+        self.embedding = nn.Parameter(torch.empty((self.k, self.d)))
+
+    def reset_weights(self):
+        """ Initialize weights of the embedding """
+        self.embedding.data.uniform_(-1.0/self.k, 1.0/self.k)
+
+    def extra_repr(self) -> str:
+        return f"k={self.k}, d={self.d}"
+
+    def forward(self, z_e_x):
+        z_e_x_ = z_e_x.permute(0, 2, 3, 1).contiguous()
+        latents = vector_quantization(z_e_x_, self.embedding)
+        return latents
+
+    def straight_through(self, z_e_x):
+        z_e_x_ = z_e_x.permute(0, 2, 3, 1).contiguous()
+        z_q_x_, indices = vector_quantization_straight_through(z_e_x_, self.embedding.detach())
+        z_q_x = z_q_x_.permute(0, 3, 1, 2).contiguous()
+
+        z_q_x_bar_flatten = torch.index_select(self.embedding, dim=0, index=indices)
+        z_q_x_bar_ = z_q_x_bar_flatten.view_as(z_e_x_)
+        z_q_x_bar = z_q_x_bar_.permute(0, 3, 1, 2).contiguous()
+
+        return z_q_x, z_q_x_bar
+
+
+class VQVAE(GradientModel):
+    """
+    Implementation of Neural Discrete Representation Learning  (https://arxiv.org/abs/1711.00937)
+    Vector-Quantised Variational-AutoEncoder (VQ-VAE)
+    """
+
+    def __init__(self, img_rows, img_cols, img_channels, channels=None, k: int = 512, d: int = 256,
+                 beta: float = 1.0):
+        super().__init__()
+
+        if channels is None:
+            channels = [16, 32, 32]
+
+        layer_series = [
+            (3, 1, 1),
+            (3, 1, 2),
+            (3, 1, 2),
+        ]
+
+        self.codebook = VQEmbedding(k, d)
+
+        self.final_width = net_util.convolutional_layer_series(img_rows, layer_series)
+        self.final_height = net_util.convolutional_layer_series(img_cols, layer_series)
+        self.channels = channels
+
+        self.beta = beta
+        self.k = k
+        self.d = d
+
+        self.encoder = nn.Sequential(
+            nn.Conv2d(in_channels=img_channels, out_channels=channels[0], kernel_size=(3, 3), padding=1),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[0],
+                net_util.convolutional_layer_series(img_rows, layer_series[:1]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:1]),
+            ]),
+            nn.Conv2d(in_channels=channels[0], out_channels=channels[1], kernel_size=(3, 3), stride=2, padding=1),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[1],
+                net_util.convolutional_layer_series(img_rows, layer_series[:2]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:2]),
+            ]),
+            nn.Conv2d(in_channels=channels[1], out_channels=channels[2], kernel_size=(3, 3), stride=2, padding=1),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[2],
+                net_util.convolutional_layer_series(img_rows, layer_series),
+                net_util.convolutional_layer_series(img_cols, layer_series),
+            ]),
+            nn.Conv2d(in_channels=channels[2], out_channels=self.d, kernel_size=(3, 3), stride=1, padding=1),
+            nn.SELU(True),
+            nn.LayerNorm([
+                self.d,
+                net_util.convolutional_layer_series(img_rows, layer_series),
+                net_util.convolutional_layer_series(img_cols, layer_series),
+            ]),
+        )
+
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose2d(in_channels=self.d, out_channels=channels[2], kernel_size=(3, 3), stride=1, padding=1),
+            # nn.Linear(d, self.final_width * self.final_height * channels[2]),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[2],
+                net_util.convolutional_layer_series(img_rows, layer_series),
+                net_util.convolutional_layer_series(img_cols, layer_series),
+            ]),
+            nn.ConvTranspose2d(
+                in_channels=channels[2], out_channels=channels[1], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[1],
+                net_util.convolutional_layer_series(img_rows, layer_series[:2]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:2]),
+            ]),
+            nn.ConvTranspose2d(
+                in_channels=channels[1], out_channels=channels[0], kernel_size=3, stride=2, padding=1, output_padding=1
+            ),
+            # nn.ReLU(True),
+            nn.SELU(True),
+            nn.LayerNorm([
+                channels[0],
+                net_util.convolutional_layer_series(img_rows, layer_series[:1]),
+                net_util.convolutional_layer_series(img_cols, layer_series[:1]),
+            ]),
+            nn.ConvTranspose2d(in_channels=channels[0], out_channels=img_channels, kernel_size=3, padding=1),
+            nn.Sigmoid()
+        )
+
+    def reset_weights(self):
+        self.codebook.reset_weights()
+
+        for m in it.chain(self.encoder, self.decoder):
+            if isinstance(m, nn.Conv2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.ConvTranspose2d):
+                self._weight_initializer(m)
+            elif isinstance(m, nn.Linear):
+                self._weight_initializer(m)
+
+    @staticmethod
+    def _weight_initializer(tensor):
+        init.xavier_uniform_(tensor.weight, gain=init.calculate_gain('relu'))
+        init.constant_(tensor.bias, 0.0)
+
+    def encode(self, x):
+        z_e_x = self.encoder(x)
+        latents = self.codebook(z_e_x)
+        return latents
+
+    def decode(self, latents):
+        z_q_x = self.codebook.embedding(latents).permute(0, 3, 1, 2)  # (B, D, H, W)
+        x_tilde = self.decoder(z_q_x)
+        return x_tilde
+
+    def forward(self, x):
+        z_e_x = self.encoder(x)
+        z_q_x_st, z_q_x = self.codebook.straight_through(z_e_x)
+        x_tilde = self.decoder(z_q_x_st)
+        return x_tilde
+
+    def calculate_gradient(self, data: dict) -> dict:
+        """
+        Calculate gradient for given batch of supervised learning.
+        Returns a dictionary of metrics
+        """
+        input_data = data['x']
+        target_data = data['y']
+
+        # x_tilde, z_e_x, z_q_x = self(input_data)
+        z_e_x = self.encoder(input_data)
+        z_q_x_st, z_q_x = self.codebook.straight_through(z_e_x)
+        x_tilde = self.decoder(z_q_x_st)
+
+        # Reconstruction loss
+        loss_recons = F.mse_loss(x_tilde, target_data)
+
+        # Vector quantization objective
+        loss_vq = F.mse_loss(z_q_x, z_e_x.detach())
+
+        # Commitment objective
+        loss_commit = F.mse_loss(z_e_x, z_q_x.detach())
+
+        loss = loss_recons + loss_vq + self.beta * loss_commit
+
+        if self.training:
+            loss.backward()
+
+        return {
+            'loss': loss.item(),
+            'reconstruction': loss_recons.item(),
+            'loss_vq': loss_vq.item(),
+            'loss_commit': loss_commit.item()
+        }
+
+    def metrics(self):
+        """ Set of metrics for this model """
+        return [
+            Loss(),
+            AveragingNamedMetric('reconstruction', scope="train"),
+            AveragingNamedMetric('loss_vq', scope="train"),
+            AveragingNamedMetric('loss_commit', scope="train"),
+            AveragingNamedMetric('grad_norm', scope="train")
+        ]
+
+
+def create(img_rows, img_cols, img_channels, channels=None, k: int = 512, d: int = 256,
+           beta: float = 1.0):
+    """ Vel factory function """
+    from vel.api import ModuleFactory
+
+    if channels is None:
+        channels = [16, 32, 32]
+
+    def instantiate(**_):
+        return VQVAE(
+            img_rows, img_cols, img_channels, channels=channels, k=k, d=d, beta=beta
+        )
+
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/rl/algo/__init__.py b/vel/model/nlp/__init__.py
similarity index 100%
rename from vel/rl/algo/__init__.py
rename to vel/model/nlp/__init__.py
diff --git a/vel/model/nlp/language_model.py b/vel/model/nlp/language_model.py
new file mode 100644
index 00000000..e45347a0
--- /dev/null
+++ b/vel/model/nlp/language_model.py
@@ -0,0 +1,77 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vel.api import LossFunctionModel, ModuleFactory, VModule, BackboneModule, SizeHints, SizeHint
+
+
+class LanguageModel(LossFunctionModel):
+    """ Language model - autoregressive generative model for text """
+
+    def __init__(self, alphabet_size: int, net: BackboneModule):
+        super().__init__()
+
+        self.alphabet_size = alphabet_size
+        self.output_dim = self.alphabet_size + 1
+
+        self.net = net
+        self.output_layer = nn.Linear(
+            in_features=self.net.size_hints().assert_single().last(),
+            out_features=self.alphabet_size+1
+        )
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return self.net.is_stateful
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        return self.net.zero_state(batch_size)
+
+    def forward(self, input_data: torch.Tensor, state=None) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+
+        Should be overridden by all subclasses.
+
+        .. note::
+            Although the recipe for forward pass needs to be defined within
+            this function, one should call the :class:`Module` instance afterwards
+            instead of this since the former takes care of running the
+            registered hooks while the latter silently ignores them.
+        """
+        if self.net.is_stateful:
+            output, new_state = self.net(input_data, state=state)
+            return F.log_softmax(self.output_layer(output), dim=-1), new_state
+        else:
+            output = self.net(input_data)
+            return F.log_softmax(self.output_layer(output), dim=-1)
+
+    def loss_value(self, x_data, y_true, y_pred) -> torch.tensor:
+        """ Calculate a value of loss function """
+        y_pred = y_pred.view(-1, y_pred.size(2))
+        y_true = y_true.view(-1).to(torch.long)
+        return F.nll_loss(y_pred, y_true)
+
+
+class LanguageModelFactory(ModuleFactory):
+    def __init__(self, alphabet_size: int, net_factory: ModuleFactory):
+        self.alphabet_size = alphabet_size
+        self.net_factory = net_factory
+
+    def instantiate(self, **extra_args) -> VModule:
+        size_hint = SizeHints(SizeHint(None, None))
+        net = self.net_factory.instantiate(alphabet_size=self.alphabet_size, size_hint=size_hint)
+
+        return LanguageModel(
+            alphabet_size=self.alphabet_size,
+            net=net
+        )
+
+
+def create(loader, net: ModuleFactory):
+    """ Vel factory function """
+    return LanguageModelFactory(
+        alphabet_size=loader.alphabet_size,
+        net_factory=net
+    )
diff --git a/vel/model/nlp/sequence_classification.py b/vel/model/nlp/sequence_classification.py
new file mode 100644
index 00000000..1aa6bd3e
--- /dev/null
+++ b/vel/model/nlp/sequence_classification.py
@@ -0,0 +1,91 @@
+import itertools as it
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vel.api import (
+    LossFunctionModel, ModuleFactory, VModule, BackboneModule, SizeHints, SizeHint, OptimizerFactory,
+    VelOptimizer
+)
+from vel.metric.accuracy import Accuracy
+from vel.metric.loss_metric import Loss
+
+
+class SequenceClassification(LossFunctionModel):
+    """ NLP (text) sequence classification """
+
+    def __init__(self, net: BackboneModule, output_size: int):
+        super().__init__()
+
+        self.net = net
+        self.output_layer = nn.Linear(
+            in_features=self.net.size_hints().assert_single().last(),
+            out_features=output_size
+        )
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return self.net.is_stateful
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        return self.net.zero_state(batch_size)
+
+    def forward(self, input_data: torch.Tensor, state=None) -> torch.Tensor:
+        r"""Defines the computation performed at every call.
+
+        Should be overridden by all subclasses.
+
+        .. note::
+            Although the recipe for forward pass needs to be defined within
+            this function, one should call the :class:`Module` instance afterwards
+            instead of this since the former takes care of running the
+            registered hooks while the latter silently ignores them.
+        """
+        if self.net.is_stateful:
+            output, new_state = self.net(input_data, state=state)
+            output = F.log_softmax(self.output_layer(output), dim=-1)
+            return output, new_state
+        else:
+            output = self.net(input_data)
+            output = F.log_softmax(self.output_layer(output), dim=-1)
+            return output
+
+    def loss_value(self, x_data, y_true, y_pred) -> torch.tensor:
+        """ Calculate a value of loss function """
+        return F.nll_loss(y_pred, y_true)
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        grouped = self.net.grouped_parameters()
+        parameters = it.chain(grouped, [("output", self.output_layer.parameters())])
+        return optimizer_factory.instantiate(parameters)
+
+    def metrics(self) -> list:
+        """ Set of metrics for this model """
+        return [Loss(), Accuracy()]
+
+
+class SequenceClassificationFactory(ModuleFactory):
+    def __init__(self, net_factory: ModuleFactory, alphabet_size: int, output_dim: int):
+        self.net_factory = net_factory
+        self.output_dim = output_dim
+        self.alphabet_size = alphabet_size
+
+    def instantiate(self, **extra_args) -> VModule:
+        size_hint = SizeHints(SizeHint(None, None))
+        net = self.net_factory.instantiate(alphabet_size=self.alphabet_size, size_hint=size_hint)
+
+        return SequenceClassification(
+            net=net, output_size=self.output_dim
+        )
+
+
+def create(loader, net: ModuleFactory, output_dim: int):
+    """ Vel factory function """
+    return SequenceClassificationFactory(
+        net_factory=net,
+        alphabet_size=loader.alphabet_size,
+        output_dim=output_dim
+    )
diff --git a/vel/rl/algo/policy_gradient/__init__.py b/vel/model/vision/__init__.py
similarity index 100%
rename from vel/rl/algo/policy_gradient/__init__.py
rename to vel/model/vision/__init__.py
diff --git a/vel/models/vision/cifar10_cnn_01.py b/vel/model/vision/cifar10_cnn_01.py
similarity index 92%
rename from vel/models/vision/cifar10_cnn_01.py
rename to vel/model/vision/cifar10_cnn_01.py
index 10eeafe8..da9e2c57 100644
--- a/vel/models/vision/cifar10_cnn_01.py
+++ b/vel/model/vision/cifar10_cnn_01.py
@@ -8,12 +8,12 @@
 import torch.nn.init as init
 import torch.nn.functional as F
 
-from vel.api import SupervisedModel, ModelFactory
-from vel.metrics.loss_metric import Loss
-from vel.metrics.accuracy import Accuracy
+from vel.api import LossFunctionModel, ModuleFactory
+from vel.metric.loss_metric import Loss
+from vel.metric.accuracy import Accuracy
 
 
-class Net(SupervisedModel):
+class Net(LossFunctionModel):
     """
     A simple MNIST classification model.
 
@@ -92,4 +92,4 @@ def create(img_rows, img_cols, img_channels, num_classes):
     """ Vel factory function """
     def instantiate(**_):
         return Net(img_rows, img_cols, img_channels, num_classes)
-    return ModelFactory.generic(instantiate)
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/models/vision/cifar_resnet_v1.py b/vel/model/vision/cifar_resnet_v1.py
similarity index 90%
rename from vel/models/vision/cifar_resnet_v1.py
rename to vel/model/vision/cifar_resnet_v1.py
index 5b638710..4a520c65 100644
--- a/vel/models/vision/cifar_resnet_v1.py
+++ b/vel/model/vision/cifar_resnet_v1.py
@@ -6,11 +6,11 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from vel.api import SupervisedModel, ModelFactory
-from vel.modules.resnet_v1 import Bottleneck, BasicBlock
+from vel.api import LossFunctionModel, ModuleFactory
+from vel.module.resnet_v1 import Bottleneck, BasicBlock
 
 
-class ResNetV1(SupervisedModel):
+class ResNetV1(LossFunctionModel):
     """ A ResNet V1 model as defined in the literature """
 
     def __init__(self, block, layers, inplanes, divisor=4, img_channels=3, num_classes=1000):
@@ -74,8 +74,8 @@ def loss_value(self, x_data, y_true, y_pred):
 
     def metrics(self):
         """ Set of metrics for this model """
-        from vel.metrics.loss_metric import Loss
-        from vel.metrics.accuracy import Accuracy
+        from vel.metric.loss_metric import Loss
+        from vel.metric.accuracy import Accuracy
         return [Loss(), Accuracy()]
 
 
@@ -89,4 +89,4 @@ def create(blocks, mode='basic', inplanes=16, divisor=4, num_classes=1000):
     def instantiate(**_):
         return ResNetV1(block_dict[mode], blocks, inplanes=inplanes, divisor=divisor, num_classes=num_classes)
 
-    return ModelFactory.generic(instantiate)
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/models/vision/cifar_resnet_v2.py b/vel/model/vision/cifar_resnet_v2.py
similarity index 86%
rename from vel/models/vision/cifar_resnet_v2.py
rename to vel/model/vision/cifar_resnet_v2.py
index bd430af8..eef5ab38 100644
--- a/vel/models/vision/cifar_resnet_v2.py
+++ b/vel/model/vision/cifar_resnet_v2.py
@@ -6,11 +6,11 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from vel.api import SupervisedModel, ModelFactory
-from vel.modules.resnet_v2 import Bottleneck, BasicBlock
+from vel.api import LossFunctionModel, ModuleFactory
+from vel.module.resnet_v2 import Bottleneck, BasicBlock
 
 
-class ResNetV2(SupervisedModel):
+class ResNetV2(LossFunctionModel):
     """ A ResNet V2 (pre-activation resnet) model as defined in the literature """
 
     def __init__(self, block, layers, inplanes, divisor=4, img_channels=3, num_classes=1000):
@@ -76,16 +76,10 @@ def loss_value(self, x_data, y_true, y_pred):
 
     def metrics(self):
         """ Set of metrics for this model """
-        from vel.metrics.loss_metric import Loss
-        from vel.metrics.accuracy import Accuracy
+        from vel.metric.loss_metric import Loss
+        from vel.metric.accuracy import Accuracy
         return [Loss(), Accuracy()]
 
-    def summary(self):
-        """ Print model summary """
-        # import torchsummary
-        # torchsummary.summary(self, input_size=(3, 32, 32))
-        print(self)
-
 
 def create(blocks, mode='basic', inplanes=16, divisor=4, num_classes=1000):
     """ Vel factory function """
@@ -97,4 +91,4 @@ def create(blocks, mode='basic', inplanes=16, divisor=4, num_classes=1000):
     def instantiate(**_):
         return ResNetV2(block_dict[mode], blocks, inplanes=inplanes, divisor=divisor, num_classes=num_classes)
 
-    return ModelFactory.generic(instantiate)
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/models/vision/cifar_resnext.py b/vel/model/vision/cifar_resnext.py
similarity index 76%
rename from vel/models/vision/cifar_resnext.py
rename to vel/model/vision/cifar_resnext.py
index 9ce14b4a..d3ce97a3 100644
--- a/vel/models/vision/cifar_resnext.py
+++ b/vel/model/vision/cifar_resnext.py
@@ -6,14 +6,15 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from vel.api import SupervisedModel, ModelFactory
-from vel.modules.resnext import ResNeXtBottleneck
+from vel.api import LossFunctionModel, ModuleFactory
+from vel.module.resnext import ResNeXtBottleneck
 
 
-class ResNeXt(SupervisedModel):
+class ResNeXt(LossFunctionModel):
     """ A ResNext model as defined in the literature """
 
-    def __init__(self, block, layers, inplanes, image_features, cardinality=4, divisor=4, img_channels=3, num_classes=1000):
+    def __init__(self, block, layers, inplanes, image_features, cardinality=4, divisor=4, img_channels=3,
+                 num_classes=1000):
         super().__init__()
 
         self.num_classess = num_classes
@@ -43,8 +44,7 @@ def __init__(self, block, layers, inplanes, image_features, cardinality=4, divis
                 nn.init.constant_(m.bias, 0.0)
 
     def _make_layer(self, block, in_channels, out_channels, blocks, stride=1):
-        layers = []
-        layers.append(block(in_channels, out_channels, self.cardinality, self.divisor, stride=stride))
+        layers = [block(in_channels, out_channels, self.cardinality, self.divisor, stride=stride)]
 
         for i in range(1, blocks):
             layers.append(block(out_channels, out_channels, self.cardinality, self.divisor, stride=1))
@@ -73,18 +73,10 @@ def loss_value(self, x_data, y_true, y_pred):
 
     def metrics(self):
         """ Set of metrics for this model """
-        from vel.metrics.loss_metric import Loss
-        from vel.metrics.accuracy import Accuracy
+        from vel.metric.loss_metric import Loss
+        from vel.metric.accuracy import Accuracy
         return [Loss(), Accuracy()]
 
-    def summary(self):
-        """ Print model summary """
-        # import torchsummary
-
-        print(self)
-        # self.eval()
-        # torchsummary.summary(self, input_size=(3, 32, 32))
-
 
 def create(blocks, mode='basic', inplanes=64, cardinality=4, image_features=64, divisor=4, num_classes=1000):
     """ Vel factory function """
@@ -94,6 +86,9 @@ def create(blocks, mode='basic', inplanes=64, cardinality=4, image_features=64,
     }
 
     def instantiate(**_):
-        return ResNeXt(block_dict[mode], blocks, inplanes=inplanes, image_features=image_features, cardinality=cardinality, divisor=divisor, num_classes=num_classes)
+        return ResNeXt(
+            block_dict[mode], blocks, inplanes=inplanes, image_features=image_features,
+            cardinality=cardinality, divisor=divisor, num_classes=num_classes
+        )
 
-    return ModelFactory.generic(instantiate)
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/models/vision/mnist_cnn_01.py b/vel/model/vision/mnist_cnn_01.py
similarity index 90%
rename from vel/models/vision/mnist_cnn_01.py
rename to vel/model/vision/mnist_cnn_01.py
index 40b2d54e..df8c5ada 100644
--- a/vel/models/vision/mnist_cnn_01.py
+++ b/vel/model/vision/mnist_cnn_01.py
@@ -8,12 +8,13 @@
 import torch.nn.init as init
 import torch.nn.functional as F
 
-from vel.api import SupervisedModel, ModelFactory
-from vel.metrics.loss_metric import Loss
-from vel.metrics.accuracy import Accuracy
 
+from vel.api import LossFunctionModel, ModuleFactory
+from vel.metric.loss_metric import Loss
+from vel.metric.accuracy import Accuracy
 
-class Net(SupervisedModel):
+
+class Net(LossFunctionModel):
     """
     A simple MNIST classification model.
 
@@ -26,11 +27,6 @@ class Net(SupervisedModel):
     Dense - output (softmax)
     """
 
-    @staticmethod
-    def _weight_initializer(tensor):
-        init.xavier_uniform_(tensor.weight, gain=init.calculate_gain('relu'))
-        init.constant_(tensor.bias, 0.0)
-
     def __init__(self, img_rows, img_cols, img_channels, num_classes):
         super(Net, self).__init__()
 
@@ -45,6 +41,11 @@ def __init__(self, img_rows, img_cols, img_channels, num_classes):
         self.dropout2 = nn.Dropout(p=0.5)
         self.fc2 = nn.Linear(128, num_classes)
 
+    @staticmethod
+    def _weight_initializer(tensor):
+        init.xavier_uniform_(tensor.weight, gain=init.calculate_gain('relu'))
+        init.constant_(tensor.bias, 0.0)
+
     def reset_weights(self):
         self._weight_initializer(self.conv1)
         self._weight_initializer(self.conv2)
@@ -76,4 +77,4 @@ def create(img_rows, img_cols, img_channels, num_classes):
     def instantiate(**_):
         return Net(img_rows, img_cols, img_channels, num_classes)
 
-    return ModelFactory.generic(instantiate)
+    return ModuleFactory.generic(instantiate)
diff --git a/vel/models/rnn/multilayer_rnn_sequence_classification.py b/vel/models/rnn/multilayer_rnn_sequence_classification.py
deleted file mode 100644
index 8542e119..00000000
--- a/vel/models/rnn/multilayer_rnn_sequence_classification.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import typing
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-
-from vel.api import SupervisedModel, ModelFactory, LinearBackboneModel
-from vel.metrics.accuracy import Accuracy
-from vel.metrics.loss_metric import Loss
-from vel.modules.rnn_layer import RnnLayer
-
-
-class MultilayerRnnSequenceClassification(SupervisedModel):
-    """ Multilayer GRU network for sequence modeling (n:1) """
-
-    def __init__(self, input_block: LinearBackboneModel, rnn_type: str, output_dim: int,
-                 rnn_layers: typing.List[int], rnn_dropout: float=0.0, bidirectional: bool=False,
-                 linear_layers: typing.List[int]=None, linear_dropout: float=0.0):
-        super().__init__()
-
-        self.output_dim = output_dim
-
-        self.rnn_layers_sizes = rnn_layers
-        self.rnn_dropout = rnn_dropout
-        self.linear_layers_sizes = linear_layers
-        self.linear_dropout = linear_dropout
-
-        self.bidirectional = bidirectional
-        self.input_block = input_block
-
-        current_dim = self.input_block.output_dim
-
-        self.rnn_layers = []
-        self.rnn_dropout_layers = []
-
-        bidirectional_multiplier = 1
-
-        for idx, current_layer in enumerate(rnn_layers, 1):
-            rnn = RnnLayer(
-                input_size=current_dim * bidirectional_multiplier,
-                hidden_size=current_layer,
-                rnn_type=rnn_type,
-                bidirectional=bidirectional,
-            )
-
-            self.add_module('{}{:02}'.format(rnn_type, idx), rnn)
-            self.rnn_layers.append(rnn)
-
-            if self.rnn_dropout > 0.0:
-                dropout_layer = nn.Dropout(p=self.rnn_dropout)
-
-                self.add_module('rnn_dropout{:02}'.format(idx), dropout_layer)
-                self.rnn_dropout_layers.append(dropout_layer)
-
-            current_dim = current_layer
-
-            if self.bidirectional:
-                bidirectional_multiplier = 2
-            else:
-                bidirectional_multiplier = 1
-
-        self.linear_layers = []
-        self.linear_dropout_layers = []
-
-        for idx, current_layer in enumerate(linear_layers, 1):
-            linear_layer = nn.Linear(current_dim * bidirectional_multiplier, current_layer)
-
-            self.add_module('linear{:02}'.format(idx), linear_layer)
-            self.linear_layers.append(linear_layer)
-
-            if self.linear_dropout > 0.0:
-                dropout_layer = nn.Dropout(p=self.linear_dropout)
-
-                self.add_module('linear_dropout{:02}'.format(idx), dropout_layer)
-                self.linear_dropout_layers.append(dropout_layer)
-
-            bidirectional_multiplier = 1
-            current_dim = current_layer
-
-        if self.bidirectional:
-            self.output_layer = nn.Linear(bidirectional_multiplier * current_dim, output_dim)
-        else:
-            self.output_layer = nn.Linear(current_dim, output_dim)
-
-        self.output_activation = nn.LogSoftmax(dim=1)
-
-    def reset_weights(self):
-        self.input_block.reset_weights()
-
-        for layer in self.linear_layers:
-            nn.init.kaiming_normal_(layer.weight, nonlinearity='relu')
-            nn.init.zeros_(layer.bias)
-
-        nn.init.kaiming_normal_(self.output_layer.weight, nonlinearity='relu')
-        nn.init.zeros_(self.output_layer.bias)
-
-    def forward(self, sequence):
-        """ Forward propagate batch of sequences through the network, without accounting for the state """
-        data = self.input_block(sequence)
-
-        for idx in range(len(self.rnn_layers)):
-            data, _ = self.rnn_layers[idx](data)
-
-            if self.rnn_dropout_layers:
-                data = self.rnn_dropout_layers[idx](data)
-
-        # We are interested only in the last element of the sequence
-        if self.bidirectional:
-            last_hidden_size = self.rnn_layers_sizes[-1]
-            data = torch.cat([data[:, -1, :last_hidden_size], data[:, 0, last_hidden_size:]], dim=1)
-        else:
-            data = data[:, -1]
-
-        for idx in range(len(self.linear_layers_sizes)):
-            data = F.relu(self.linear_layers[idx](data))
-
-            if self.linear_dropout_layers:
-                data = self.linear_dropout_layers[idx](data)
-
-        data = self.output_layer(data)
-
-        return self.output_activation(data)
-
-    def get_layer_groups(self):
-        return [
-            self.input_block,
-            self.rnn_layers,
-            self.linear_layers,
-            self.output_layer
-        ]
-
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
-        return sum(x.state_dim for x in self.gru_layers)
-
-    def loss_value(self, x_data, y_true, y_pred):
-        """ Calculate a value of loss function """
-        return F.nll_loss(y_pred, y_true)
-
-    def metrics(self) -> list:
-        """ Set of metrics for this model """
-        return [Loss(), Accuracy()]
-
-
-def create(input_block: ModelFactory, rnn_type: str, output_dim: int,
-           rnn_layers: typing.List[int], rnn_dropout: float=0.0, bidirectional: bool=False,
-           linear_layers: typing.List[int]=None, linear_dropout: float=0.0):
-    """ Vel factory function """
-    if linear_layers is None:
-        linear_layers = []
-
-    def instantiate(**_):
-        return MultilayerRnnSequenceClassification(
-            input_block=input_block.instantiate(), rnn_type=rnn_type, output_dim=output_dim,
-            rnn_layers=rnn_layers, rnn_dropout=rnn_dropout, bidirectional=bidirectional,
-            linear_layers=linear_layers, linear_dropout=linear_dropout
-        )
-
-    return ModelFactory.generic(instantiate)
diff --git a/vel/models/rnn/multilayer_rnn_sequence_model.py b/vel/models/rnn/multilayer_rnn_sequence_model.py
deleted file mode 100644
index 9352ef10..00000000
--- a/vel/models/rnn/multilayer_rnn_sequence_model.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import typing
-
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-
-from vel.api import RnnSupervisedModel, ModelFactory, LinearBackboneModel
-from vel.modules.rnn_layer import RnnLayer
-
-
-class MultilayerRnnSequenceModel(RnnSupervisedModel):
-    """ Multilayer GRU network for sequence modeling (n:n) """
-
-    def __init__(self, input_block: LinearBackboneModel, rnn_type: str, hidden_layers: typing.List[int],
-                 output_dim: int, dropout: float=0.0):
-        super().__init__()
-
-        self.output_dim = output_dim
-        self.hidden_layers = hidden_layers
-
-        self.input_block = input_block
-
-        current_dim = self.input_block.output_dim
-
-        self.recurrent_layers = []
-        self.dropout_layers = []
-
-        for idx, current_layer in enumerate(hidden_layers, 1):
-            rnn = RnnLayer(
-                input_size=current_dim,
-                hidden_size=current_layer,
-                rnn_type=rnn_type,
-            )
-
-            self.add_module('{}{:02}'.format(rnn_type, idx), rnn)
-            self.recurrent_layers.append(rnn)
-
-            if dropout > 0.0:
-                dropout_layer = nn.Dropout(p=dropout)
-
-                self.add_module('rnn_dropout{:02}'.format(idx), dropout_layer)
-                self.dropout_layers.append(dropout_layer)
-
-            current_dim = current_layer
-
-        self.output_layer = nn.Linear(current_dim, output_dim)
-        self.output_activation = nn.LogSoftmax(dim=2)
-
-    def reset_weights(self):
-        self.input_block.reset_weights()
-
-    def forward(self, sequence):
-        """ Forward propagate batch of sequences through the network, without accounting for the state """
-        data = self.input_block(sequence)
-
-        for idx in range(len(self.recurrent_layers)):
-            data, _ = self.recurrent_layers[idx](data)
-
-            if self.dropout_layers:
-                data = self.dropout_layers[idx](data)
-
-        data = self.output_layer(data)
-
-        return self.output_activation(data)
-
-    def forward_state(self, sequence, state=None):
-        """ Forward propagate a sequence through the network accounting for the state """
-        if state is None:
-            state = self.zero_state(sequence.size(0))
-
-        data = self.input_block(sequence)
-
-        state_outputs = []
-
-        # for layer_length, layer in zip(self.hidden_layers, self.recurrent_layers):
-        for idx in range(len(self.recurrent_layers)):
-            layer_length = self.recurrent_layers[idx].state_dim
-
-            # Partition hidden state, for each layer we have layer_length of h state and layer_length of c state
-            current_state = state[:, :, :layer_length]
-            state = state[:, :, layer_length:]
-
-            # Propagate through the GRU state
-            data, new_h = self.recurrent_layers[idx](data, current_state)
-
-            if self.dropout_layers:
-                data = self.dropout_layers[idx](data)
-
-            state_outputs.append(new_h)
-
-        output_data = self.output_activation(self.output_layer(data))
-
-        concatenated_hidden_output = torch.cat(state_outputs, dim=2)
-
-        return output_data, concatenated_hidden_output
-
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
-        return sum(x.state_dim for x in self.recurrent_layers)
-
-    def zero_state(self, batch_size):
-        """ Initial state of the network """
-        return torch.zeros(1, batch_size, self.state_dim)
-
-    def loss_value(self, x_data, y_true, y_pred):
-        """ Calculate a value of loss function """
-        y_pred = y_pred.view(-1, y_pred.size(2))
-        y_true = y_true.view(-1).to(torch.long)
-        return F.nll_loss(y_pred, y_true)
-
-
-def create(input_block: ModelFactory, rnn_type: str, hidden_layers: typing.List[int],
-           output_dim: int, dropout=0.0):
-    """ Vel factory function """
-    def instantiate(**_):
-        return MultilayerRnnSequenceModel(
-            input_block.instantiate(), rnn_type=rnn_type, hidden_layers=hidden_layers, output_dim=output_dim, dropout=dropout
-        )
-
-    return ModelFactory.generic(instantiate)
diff --git a/vel/rl/buffers/__init__.py b/vel/module/__init__.py
similarity index 100%
rename from vel/rl/buffers/__init__.py
rename to vel/module/__init__.py
diff --git a/vel/rl/buffers/backend/__init__.py b/vel/module/input/__init__.py
similarity index 100%
rename from vel/rl/buffers/backend/__init__.py
rename to vel/module/input/__init__.py
diff --git a/vel/module/input/image_to_tensor.py b/vel/module/input/image_to_tensor.py
new file mode 100644
index 00000000..3ca95d3c
--- /dev/null
+++ b/vel/module/input/image_to_tensor.py
@@ -0,0 +1,26 @@
+import torch
+
+from vel.api import VModule
+
+
+def image_to_tensor(image: torch.Tensor) -> torch.Tensor:
+    """ Convert pytorch image (b, w, h, c) into tensor (b, c, w, h) float32 """
+    result = image.permute(0, 3, 1, 2).contiguous()
+
+    if result.dtype == torch.uint8:
+        result = result.type(torch.float) / 255.0
+    else:
+        result = result.type(torch.float)
+
+    return result
+
+
+class ImageToTensor(VModule):
+    """
+    Convert simple image to tensor.
+
+    Flip channels to a [C, W, H] order and potentially convert 8-bit color values to floats
+    """
+
+    def forward(self, image):
+        return image_to_tensor(image)
diff --git a/vel/module/input/normalize_ewma.py b/vel/module/input/normalize_ewma.py
new file mode 100644
index 00000000..0e59b792
--- /dev/null
+++ b/vel/module/input/normalize_ewma.py
@@ -0,0 +1,47 @@
+import torch
+
+from vel.api import VModule
+
+
+class NormalizeEwma(VModule):
+    """ Normalize a vector of observations - across the batch dim """
+
+    def __init__(self, input_shape, beta=0.99, per_element_update=False, epsilon=1e-1):
+        super().__init__()
+
+        self.input_shape = input_shape
+        self.epsilon = epsilon
+        self.beta = beta
+        self.per_element_update = per_element_update
+
+        self.register_buffer('running_mean', torch.zeros(input_shape, dtype=torch.float))
+        self.register_buffer('running_var', torch.ones(input_shape, dtype=torch.float))
+        self.register_buffer('debiasing_term', torch.tensor(self.epsilon, dtype=torch.float))
+
+    def reset_weights(self):
+        self.running_mean.zero_()
+        self.running_var.fill_(1.0)
+        self.debiasing_term.fill_(self.epsilon)
+
+    def forward(self, input_vector):
+        # Make sure input is float32
+        input_vector = input_vector.to(torch.float)
+
+        if self.training:
+            batch_mean = input_vector.mean(dim=0)
+            batch_var = input_vector.var(dim=0, unbiased=False)
+
+            if self.per_element_update:
+                batch_size = input_vector.size(0)
+                weight = self.beta ** batch_size
+            else:
+                weight = self.beta
+
+            self.running_mean.mul_(weight).add_(batch_mean * (1.0 - weight))
+            self.running_var.mul_(weight).add_(batch_var * (1.0 - weight))
+            self.debiasing_term.mul_(weight).add_(1.0 * (1.0 - weight))
+
+        debiased_mean = self.running_mean / self.debiasing_term
+        debiased_var = self.running_var / self.debiasing_term
+
+        return (input_vector - debiased_mean.unsqueeze(0)) / torch.sqrt(debiased_var.unsqueeze(0))
diff --git a/vel/modules/input/normalize_observations.py b/vel/module/input/normalize_expanding.py
similarity index 72%
rename from vel/modules/input/normalize_observations.py
rename to vel/module/input/normalize_expanding.py
index 914cb5f6..b649594f 100644
--- a/vel/modules/input/normalize_observations.py
+++ b/vel/module/input/normalize_expanding.py
@@ -1,11 +1,10 @@
 import torch
-import numbers
 
-from vel.api import BackboneModel, ModelFactory
+from vel.api import VModule
 
 
-class NormalizeObservations(BackboneModel):
-    """ Normalize a vector of observations """
+class NormalizeExpanding(VModule):
+    """ Normalize a vector of observations - across the batch dim """
 
     def __init__(self, input_shape, epsilon=1e-6):
         super().__init__()
@@ -45,20 +44,3 @@ def forward(self, input_vector):
             self.running_var.copy_(new_var)
 
         return (input_vector - self.running_mean.unsqueeze(0)) / torch.sqrt(self.running_var.unsqueeze(0))
-
-
-def create(input_shape):
-    """ Vel factory function """
-    if isinstance(input_shape, numbers.Number):
-        input_shape = (input_shape,)
-    elif not isinstance(input_shape, tuple):
-        input_shape = tuple(input_shape)
-
-    def instantiate(**_):
-        return NormalizeObservations(input_shape)
-
-    return ModelFactory.generic(instantiate)
-
-
-# Scripting interface
-NormalizeObservationsFactory = create
diff --git a/vel/module/input/one_hot_encoding.py b/vel/module/input/one_hot_encoding.py
new file mode 100644
index 00000000..0bbc5f52
--- /dev/null
+++ b/vel/module/input/one_hot_encoding.py
@@ -0,0 +1,16 @@
+from vel.api import VModule
+from vel.module.layers import OneHotEncode
+
+
+class OneHotEncodingInput(VModule):
+    """ One-hot encoding input layer """
+
+    def __init__(self, alphabet_size: int):
+        super().__init__()
+
+        self._alphabet_size = alphabet_size
+
+        self.layer = OneHotEncode(self._alphabet_size)
+
+    def forward(self, input_data):
+        return self.layer(input_data)
diff --git a/vel/modules/layers.py b/vel/module/layers.py
similarity index 72%
rename from vel/modules/layers.py
rename to vel/module/layers.py
index 6b694334..9f9de68d 100644
--- a/vel/modules/layers.py
+++ b/vel/module/layers.py
@@ -39,6 +39,22 @@ def forward(self, x):
         return x.view(x.size(0), -1)
 
 
+class Reshape(nn.Module):
+    """ Flatten input vector """
+    def __init__(self, *sizes, batch_dims=1):
+        super().__init__()
+
+        self.sizes = sizes
+        self.batch_dims = batch_dims
+
+    def forward(self, x):
+        return x.view(x.shape[:self.batch_dims] + self.sizes)
+
+    def extra_repr(self) -> str:
+        """ Extra representation of this module """
+        return f"sizes={self.sizes}, batch_dims={self.batch_dims}"
+
+
 class OneHotEncode(nn.Module):
     """ One-hot encoding layer """
     def __init__(self, num_classes):
@@ -47,4 +63,3 @@ def __init__(self, num_classes):
 
     def forward(self, x):
         return one_hot_encoding(x, self.num_classes)
-
diff --git a/vel/modules/resnet_v1.py b/vel/module/resnet_v1.py
similarity index 100%
rename from vel/modules/resnet_v1.py
rename to vel/module/resnet_v1.py
diff --git a/vel/modules/resnet_v2.py b/vel/module/resnet_v2.py
similarity index 95%
rename from vel/modules/resnet_v2.py
rename to vel/module/resnet_v2.py
index 31445f35..8a7e2518 100644
--- a/vel/modules/resnet_v2.py
+++ b/vel/module/resnet_v2.py
@@ -81,7 +81,9 @@ def __init__(self, in_channels, out_channels, stride=1, divisor=4):
         self.conv1 = nn.Conv2d(in_channels, self.bottleneck_channels, kernel_size=1, bias=False)
 
         self.bn2 = nn.BatchNorm2d(self.bottleneck_channels)
-        self.conv2 = nn.Conv2d(self.bottleneck_channels, self.bottleneck_channels, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.conv2 = nn.Conv2d(
+            self.bottleneck_channels, self.bottleneck_channels, kernel_size=3, stride=stride, padding=1, bias=False
+        )
 
         self.bn3 = nn.BatchNorm2d(self.bottleneck_channels)
         self.conv3 = nn.Conv2d(self.bottleneck_channels, out_channels, kernel_size=1, bias=False)
diff --git a/vel/modules/resnext.py b/vel/module/resnext.py
similarity index 99%
rename from vel/modules/resnext.py
rename to vel/module/resnext.py
index 3ae26e49..7e943402 100644
--- a/vel/modules/resnext.py
+++ b/vel/module/resnext.py
@@ -40,7 +40,6 @@ def __init__(self, in_channels, out_channels, cardinality, divisor, stride=1):
         self.conv_expand = nn.Conv2d(D * C, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
         self.bn_expand = nn.BatchNorm2d(out_channels)
 
-
     def forward(self, x):
         bottleneck = self.conv_reduce(x)
         bottleneck = F.relu(self.bn_reduce(bottleneck), inplace=True)
diff --git a/vel/module/rnn_layer.py b/vel/module/rnn_layer.py
new file mode 100644
index 00000000..1e7a8fa4
--- /dev/null
+++ b/vel/module/rnn_layer.py
@@ -0,0 +1,80 @@
+# Temporarily commented out as it's an invalid code at the moment, may be deleted later
+# import torch
+# import torch.nn as nn
+# import torch.nn.init as init
+#
+#
+# from vel.api import LinearBackboneModel
+#
+#
+# class RnnLayer(LinearBackboneModel):
+#     """ Generalization of RNN layer (Simple RNN, LSTM or GRU) """
+#
+#     def __init__(self, input_size, hidden_size, rnn_type, bias=True, bidirectional=False, nonlinearity='tanh'):
+#         super().__init__()
+#
+#         assert rnn_type in {'rnn', 'lstm', 'gru'}, "RNN type {} is not supported".format(rnn_type)
+#
+#         self.input_size = input_size
+#         self.hidden_size = hidden_size
+#         self.rnn_type = rnn_type
+#         self.bidirectional = bidirectional
+#
+#         if self.rnn_type == 'rnn':
+#             self.rnn_cell = nn.RNN(
+#                 input_size=input_size, hidden_size=hidden_size, bias=bias, nonlinearity=nonlinearity,
+#                 bidirectional=bidirectional, batch_first=True
+#             )
+#         elif self.rnn_type == 'lstm':
+#             self.rnn_cell = nn.LSTM(
+#                 input_size=input_size, hidden_size=hidden_size, bias=bias,
+#                 bidirectional=bidirectional, batch_first=True
+#             )
+#         elif self.rnn_type == 'gru':
+#             self.rnn_cell = nn.GRU(
+#                 input_size=input_size, hidden_size=hidden_size, bias=bias,
+#                 bidirectional=bidirectional, batch_first=True
+#             )
+#
+#     def reset_weights(self):
+#         init.xavier_normal_(self.rnn_cell.weight_hh)
+#         init.xavier_normal_(self.rnn_cell.weight_ih)
+#         init.zeros_(self.rnn_cell.bias_ih)
+#         init.zeros_(self.rnn_cell.bias_hh)
+#
+#     @property
+#     def output_dim(self) -> int:
+#         """ Final dimension of model output """
+#         if self.bidirectional:
+#             return 2.0 * self.hidden_size
+#         else:
+#             return self.hidden_size
+#
+#     @property
+#     def state_dim(self) -> int:
+#         """ Dimension of model state """
+#         if self.rnn_type == 'lstm':
+#             return 2 * self.hidden_size
+#         else:
+#             return self.hidden_size
+#
+#     def zero_state(self, batch_size):
+#         """ State for the model """
+#         return torch.zeros(batch_size, self.state_dim)
+#
+#     def forward(self, input_data, state=None):
+#         if state is None:
+#             if self.bidirectional:
+#                 state = self.zero_state(input_data.size(0)).unsqueeze(0).repeat(2, 1, 1).to(input_data.device)
+#             else:
+#                 state = self.zero_state(input_data.size(0)).unsqueeze(0).to(input_data.device)
+#
+#         if self.rnn_type == 'lstm':
+#             hidden_state, cell_state = torch.split(state, self.hidden_size, 2)
+#             hidden_state = hidden_state.contiguous()
+#             cell_state = cell_state.contiguous()
+#             output, (hidden_state, cell_state) = self.rnn_cell(input_data, (hidden_state, cell_state))
+#             new_state = torch.cat([hidden_state, cell_state], dim=2)
+#             return output, new_state
+#         else:
+#             return self.rnn_cell(input_data, state)
diff --git a/vel/modules/input/embedding.py b/vel/modules/input/embedding.py
deleted file mode 100644
index ff7d790c..00000000
--- a/vel/modules/input/embedding.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch.nn as nn
-
-from vel.api import LinearBackboneModel, TextData, ModelFactory
-
-
-class EmbeddingInput(LinearBackboneModel):
-    """ Learnable Embedding input layer """
-
-    def __init__(self, alphabet_size: int, output_dim: int, pretrained: bool=False, frozen: bool=False,
-                 source: TextData=None):
-        super().__init__()
-
-        self._output_dim = output_dim
-        self._alphabet_size = alphabet_size
-        self._pretrained = pretrained
-        self._frozen = frozen
-        self._source = source
-
-        self.layer = nn.Embedding(self._alphabet_size, self._output_dim)
-
-    def reset_weights(self):
-        if self._pretrained:
-            self.layer.weight.data.copy_(self._source.data_field.vocab.vectors)
-
-        if self._frozen:
-            self.layer.weight.requires_grad = False
-
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self._output_dim
-
-    def forward(self, input_data):
-        return self.layer(input_data)
-
-
-def create(alphabet_size: int, output_dim: int, pretrained: bool=False, frozen: bool=False, source: TextData=None):
-    """ Vel factory function """
-    def instantiate(**_):
-        return EmbeddingInput(alphabet_size, output_dim, pretrained=pretrained, frozen=frozen, source=source)
-
-    return ModelFactory.generic(instantiate)
-
-
-# Scripting interface
-EmbeddingInputFactory = create
-
diff --git a/vel/modules/input/identity.py b/vel/modules/input/identity.py
deleted file mode 100644
index 7018051e..00000000
--- a/vel/modules/input/identity.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from vel.api import BackboneModel, ModelFactory
-
-
-class Identity(BackboneModel):
-    """ Identity transformation that doesn't do anything """
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return x
-
-    def reset_weights(self):
-        pass
-
-
-def create():
-    """ Vel factory function """
-    def instantiate(**_):
-        return Identity()
-
-    return ModelFactory.generic(instantiate)
-
-
-# Scripting interface
-IdentityFactory = create
diff --git a/vel/modules/input/image_to_tensor.py b/vel/modules/input/image_to_tensor.py
deleted file mode 100644
index 13b58ebd..00000000
--- a/vel/modules/input/image_to_tensor.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-
-from vel.api import BackboneModel, ModelFactory
-
-
-class ImageToTensor(BackboneModel):
-    """
-    Convert simple image to tensor.
-
-    Flip channels to a [C, W, H] order and potentially convert 8-bit color values to floats
-    """
-
-    def __init__(self):
-        super().__init__()
-
-    def reset_weights(self):
-        pass
-
-    def forward(self, image):
-        result = image.permute(0, 3, 1, 2).contiguous()
-
-        if result.dtype == torch.uint8:
-            result = result.type(torch.float) / 255.0
-        else:
-            result = result.type(torch.float)
-
-        return result
-
-
-def create():
-    """ Vel factory function """
-    return ModelFactory.generic(ImageToTensor)
-
-
-# Scripting interface
-ImageToTensorFactory = create
diff --git a/vel/modules/input/one_hot_encoding.py b/vel/modules/input/one_hot_encoding.py
deleted file mode 100644
index 1e351721..00000000
--- a/vel/modules/input/one_hot_encoding.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from vel.api import LinearBackboneModel, ModelFactory
-from vel.modules.layers import OneHotEncode
-
-
-class OneHotEncodingInput(LinearBackboneModel):
-    """ One-hot encoding input layer """
-
-    def __init__(self, alphabet_size: int):
-        super().__init__()
-
-        self._alphabet_size = alphabet_size
-
-        self.layer = OneHotEncode(self._alphabet_size)
-
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self._alphabet_size
-
-    def forward(self, input_data):
-        return self.layer(input_data)
-
-
-def create(alphabet_size: int):
-    """ Vel factory function """
-    def instantiate(**_):
-        return OneHotEncodingInput(alphabet_size)
-
-    return ModelFactory.generic(instantiate)
-
-
-# Scripting interface
-OneHotEncodingInputFactory = create
diff --git a/vel/modules/rnn_cell.py b/vel/modules/rnn_cell.py
deleted file mode 100644
index 3ee521fb..00000000
--- a/vel/modules/rnn_cell.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-
-
-from vel.api import RnnLinearBackboneModel
-
-
-class RnnCell(RnnLinearBackboneModel):
-    """ Generalization of RNN cell (Simple RNN, LSTM or GRU) """
-
-    def __init__(self, input_size, hidden_size, rnn_type, bias=True, nonlinearity='tanh'):
-        super().__init__()
-
-        assert rnn_type in {'rnn', 'lstm', 'gru'}, "Rnn type {} is not supported".format(rnn_type)
-
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.rnn_type = rnn_type
-
-        if self.rnn_type == 'rnn':
-            self.rnn_cell = nn.RNNCell(
-                input_size=input_size, hidden_size=hidden_size, bias=bias, nonlinearity=nonlinearity
-            )
-        elif self.rnn_type == 'lstm':
-            self.rnn_cell = nn.LSTMCell(input_size=input_size, hidden_size=hidden_size, bias=bias)
-        elif self.rnn_type == 'gru':
-            self.rnn_cell = nn.GRUCell(input_size=input_size, hidden_size=hidden_size, bias=bias)
-
-    def reset_weights(self):
-        init.xavier_normal_(self.rnn_cell.weight_hh)
-        init.xavier_normal_(self.rnn_cell.weight_ih)
-        init.zeros_(self.rnn_cell.bias_ih)
-        init.zeros_(self.rnn_cell.bias_hh)
-
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self.hidden_size
-
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
-        if self.rnn_type == 'lstm':
-            return 2 * self.hidden_size
-        else:
-            return self.hidden_size
-
-    def forward(self, input_data, state):
-        if self.rnn_type == 'lstm':
-            hidden_state, cell_state = torch.split(state, self.hidden_size, 1)
-            hidden_state, cell_state = self.rnn_cell(input_data, (hidden_state, cell_state))
-            new_state = torch.cat([hidden_state, cell_state], dim=1)
-            return hidden_state, new_state
-        else:
-            new_hidden_state = self.rnn_cell(input_data, state)
-            return new_hidden_state, new_hidden_state
-
-
-
diff --git a/vel/modules/rnn_layer.py b/vel/modules/rnn_layer.py
deleted file mode 100644
index 5c5f6886..00000000
--- a/vel/modules/rnn_layer.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-
-
-from vel.api import RnnLinearBackboneModel
-
-
-class RnnLayer(RnnLinearBackboneModel):
-    """ Generalization of RNN layer (Simple RNN, LSTM or GRU) """
-
-    def __init__(self, input_size, hidden_size, rnn_type, bias=True, bidirectional=False, nonlinearity='tanh'):
-        super().__init__()
-
-        assert rnn_type in {'rnn', 'lstm', 'gru'}, "RNN type {} is not supported".format(rnn_type)
-
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.rnn_type = rnn_type
-        self.bidirectional = bidirectional
-
-        if self.rnn_type == 'rnn':
-            self.rnn_cell = nn.RNN(
-                input_size=input_size, hidden_size=hidden_size, bias=bias, nonlinearity=nonlinearity,
-                bidirectional=bidirectional, batch_first=True
-            )
-        elif self.rnn_type == 'lstm':
-            self.rnn_cell = nn.LSTM(
-                input_size=input_size, hidden_size=hidden_size, bias=bias,
-                bidirectional=bidirectional, batch_first=True
-            )
-        elif self.rnn_type == 'gru':
-            self.rnn_cell = nn.GRU(
-                input_size=input_size, hidden_size=hidden_size, bias=bias,
-                bidirectional=bidirectional, batch_first=True
-            )
-
-    def reset_weights(self):
-        init.xavier_normal_(self.rnn_cell.weight_hh)
-        init.xavier_normal_(self.rnn_cell.weight_ih)
-        init.zeros_(self.rnn_cell.bias_ih)
-        init.zeros_(self.rnn_cell.bias_hh)
-
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        if self.bidirectional:
-            return 2.0 * self.hidden_size
-        else:
-            return self.hidden_size
-
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
-        if self.rnn_type == 'lstm':
-            return 2 * self.hidden_size
-        else:
-            return self.hidden_size
-
-    def forward(self, input_data, state=None):
-        if state is None:
-            if self.bidirectional:
-                state = self.zero_state(input_data.size(0)).unsqueeze(0).repeat(2, 1, 1).to(input_data.device)
-            else:
-                state = self.zero_state(input_data.size(0)).unsqueeze(0).to(input_data.device)
-
-        if self.rnn_type == 'lstm':
-            hidden_state, cell_state = torch.split(state, self.hidden_size, 2)
-            hidden_state = hidden_state.contiguous()
-            cell_state = cell_state.contiguous()
-            output, (hidden_state, cell_state) = self.rnn_cell(input_data, (hidden_state, cell_state))
-            new_state = torch.cat([hidden_state, cell_state], dim=2)
-            return output, new_state
-        else:
-            return self.rnn_cell(input_data, state)
-
-
-
diff --git a/vel/rl/buffers/tests/__init__.py b/vel/net/__init__.py
similarity index 100%
rename from vel/rl/buffers/tests/__init__.py
rename to vel/net/__init__.py
diff --git a/vel/rl/commands/__init__.py b/vel/net/layer/__init__.py
similarity index 100%
rename from vel/rl/commands/__init__.py
rename to vel/net/layer/__init__.py
diff --git a/vel/rl/models/__init__.py b/vel/net/layer/arch/__init__.py
similarity index 100%
rename from vel/rl/models/__init__.py
rename to vel/net/layer/arch/__init__.py
diff --git a/vel/net/layer/arch/parallel.py b/vel/net/layer/arch/parallel.py
new file mode 100644
index 00000000..241933f5
--- /dev/null
+++ b/vel/net/layer/arch/parallel.py
@@ -0,0 +1,106 @@
+import torch.nn as nn
+import typing
+
+from vel.api import SizeHints
+from vel.net.layer_base import LayerFactory, Layer, LayerInfo, LayerFactoryContext
+
+
+class ParallelLayer(Layer):
+    """ Network that consists of parallel "towers" """
+
+    def __init__(self, info: LayerInfo, layers: [Layer]):
+        super().__init__(info)
+
+        self.layers = nn.ModuleList(layers)
+        self._size_hints = SizeHints(tuple(layer.size_hints().unwrap() for layer in self.layers))
+        self._is_stateful = any(l.is_stateful for l in self.layers)
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return self._is_stateful
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self._size_hints
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        """ Forward propagation of a single layer """
+        if self._is_stateful:
+            results = []
+            output_state = {}
+
+            for layer, layer_input in zip(self.layers, direct):
+                data, new_state = layer(layer_input, state=state, context=context)
+                results.append(data)
+                output_state.update(new_state)
+
+            return tuple(results), output_state
+        else:
+            results = [layer(x, state, context) for layer, x in zip(self.layers, direct)]
+            return tuple(results)
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        zero_state = {}
+
+        for l in self.layers:
+            if l.is_stateful:
+                layer_zero_state = l.zero_state(batch_size)
+                if layer_zero_state is not None:
+                    zero_state.update(layer_zero_state)
+
+        return zero_state
+
+    def grouped_parameters(self) -> typing.Iterable[typing.Tuple[str, object]]:
+        """ Return iterable of pairs (group, parameters) """
+        raise NotImplementedError
+
+
+class ParallelLayerFactory(LayerFactory):
+    """ Factory for Parallel layer """
+
+    def __init__(self, layers: [LayerFactory]):
+        super().__init__()
+        self.layers = layers
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "parallel"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        hints = direct_input.assert_tuple(len(self.layers))
+
+        layers = []
+
+        info = self.make_info(context)
+
+        for idx, (size_hint, layer_factory) in enumerate(zip(hints, self.layers)):
+            counter = idx + 1
+
+            child_context = LayerFactoryContext(
+                idx=counter,
+                parent_group=info.group,
+                parent_name=info.name,
+                data=context.data
+            )
+
+            layer = layer_factory.instantiate(
+                direct_input=SizeHints(size_hint),
+                context=child_context,
+                extra_args=extra_args
+            )
+
+            layers.append(layer)
+
+        return ParallelLayer(
+            info=info,
+            layers=layers
+        )
+
+
+def create(layers: [LayerFactory], label=None, group=None):
+    """ Vel factory function """
+    return ParallelLayerFactory(layers=layers).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/arch/sequence.py b/vel/net/layer/arch/sequence.py
new file mode 100644
index 00000000..6c2beb30
--- /dev/null
+++ b/vel/net/layer/arch/sequence.py
@@ -0,0 +1,77 @@
+import collections
+
+from vel.api import SizeHints
+from vel.net.layer_base import LayerFactory, Layer, LayerInfo, LayerFactoryContext
+from vel.net.modular import LayerList
+
+
+class SequenceLayer(Layer):
+    """ Container around a skip connection """
+
+    def __init__(self, info: LayerInfo, layers: [Layer]):
+        super().__init__(info)
+
+        self.layers = LayerList(layers)
+
+    @property
+    def is_stateful(self) -> bool:
+        return self.layers.is_stateful
+
+    def zero_state(self, batch_size):
+        return self.layers.zero_state(batch_size)
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.layers[-1].size_hints()
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        """ Forward propagation of a single layer """
+        return self.layers(direct, state=state, context=context)
+
+
+class SequenceFactory(LayerFactory):
+    """ Factory for skip connection layers """
+
+    def __init__(self, layers: [LayerFactory]):
+        super().__init__()
+        self.layers = layers
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "skip_connection"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        loop_size_hints = direct_input
+
+        layers = collections.OrderedDict()
+
+        info = self.make_info(context)
+
+        for idx,  layer_factory in enumerate(self.layers):
+            counter = idx + 1
+
+            child_context = LayerFactoryContext(
+                idx=counter,
+                parent_group=info.group,
+                parent_name=info.name,
+                data=context.data
+            )
+
+            layer = layer_factory.instantiate(
+                direct_input=loop_size_hints,
+                context=child_context,
+                extra_args=extra_args
+            )
+
+            loop_size_hints = layer.size_hints()
+
+            layers[layer.name] = layer
+
+        return SequenceLayer(info, layers=layers)
+
+
+def create(layers: [LayerFactory], label=None, group=None):
+    """ Vel factory function """
+    return SequenceFactory(layers=layers).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/arch/skip_connection.py b/vel/net/layer/arch/skip_connection.py
new file mode 100644
index 00000000..bc7cab8a
--- /dev/null
+++ b/vel/net/layer/arch/skip_connection.py
@@ -0,0 +1,83 @@
+import collections
+
+from vel.api import SizeHints, SizeHint
+from vel.net.layer_base import LayerFactory, Layer, LayerInfo, LayerFactoryContext
+from vel.net.modular import LayerList
+
+
+class SkipConnectionLayer(Layer):
+    """ Container around a skip connection """
+
+    def __init__(self, info: LayerInfo, layers: [Layer], size_hint: SizeHint):
+        super().__init__(info)
+
+        self.layers = LayerList(layers)
+        self._size_hints = SizeHints(size_hint)
+
+    @property
+    def is_stateful(self) -> bool:
+        return self.layers.is_stateful
+
+    def zero_state(self, batch_size):
+        return self.layers.zero_state(batch_size)
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self._size_hints
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        """ Forward propagation of a single layer """
+        if self.is_stateful:
+            result, out_state = self.layers(direct, state=state, context=context)
+            return direct + result, out_state
+        else:
+            result = self.layers(direct, state=state, context=context)
+            return direct + result
+
+
+class SkipConnectionLayerFactory(LayerFactory):
+    """ Factory for skip connection layers """
+
+    def __init__(self, layers: [LayerFactory]):
+        super().__init__()
+        self.layers = layers
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "skip_connection"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        size_hint = loop_size_hint = direct_input.assert_single()
+
+        layers = collections.OrderedDict()
+
+        info = self.make_info(context)
+
+        for idx,  layer_factory in enumerate(self.layers):
+            counter = idx + 1
+
+            child_context = LayerFactoryContext(
+                idx=counter,
+                parent_group=info.group,
+                parent_name=info.name,
+                data=context.data
+            )
+
+            layer = layer_factory.instantiate(
+                direct_input=SizeHints(loop_size_hint),
+                context=child_context,
+                extra_args=extra_args
+            )
+
+            loop_size_hint = layer.size_hints().assert_single()
+
+            layers[layer.name] = layer
+
+        return SkipConnectionLayer(info, layers=layers, size_hint=size_hint)
+
+
+def create(layers: [LayerFactory], label=None, group=None):
+    """ Vel factory function """
+    return SkipConnectionLayerFactory(layers=layers).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/dropout.py b/vel/net/layer/dropout.py
new file mode 100644
index 00000000..ec68f9aa
--- /dev/null
+++ b/vel/net/layer/dropout.py
@@ -0,0 +1,55 @@
+import torch.nn.functional as F
+from vel.api import SizeHints
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
+
+
+class DropoutLayer(Layer):
+    """
+    During training, randomly zeroes some of the elements of the input
+    tensor with probability :attr:`p` using samples from a Bernoulli
+    distribution.
+
+    See :class:`~torch.nn.Dropout` for details.
+    """
+    def __init__(self, info: LayerInfo, input_shape: SizeHints, p: float):
+        super().__init__(info)
+
+        self.p = p
+        self.input_shape = input_shape
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return F.dropout(direct, p=self.p, training=self.training)
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.input_shape
+
+    def extra_repr(self) -> str:
+        """Set the extra representation of the module"""
+        return "p={:.2f}".format(self.p)
+
+
+class DropoutLayerFactory(LayerFactory):
+    """ Factory class for the Dropout layer """
+
+    def __init__(self, p: float):
+        super().__init__()
+        self.p = p
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "dropout"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        return DropoutLayer(
+            info=self.make_info(context),
+            input_shape=direct_input,
+            p=self.p
+        )
+
+
+def create(p: float, label=None, group=None):
+    """ Vel factory function """
+    return DropoutLayerFactory(p).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/models/backbone/__init__.py b/vel/net/layer/input/__init__.py
similarity index 100%
rename from vel/rl/models/backbone/__init__.py
rename to vel/net/layer/input/__init__.py
diff --git a/vel/net/layer/input/image_to_tensor.py b/vel/net/layer/input/image_to_tensor.py
new file mode 100644
index 00000000..ed073f7f
--- /dev/null
+++ b/vel/net/layer/input/image_to_tensor.py
@@ -0,0 +1,53 @@
+from vel.api import SizeHints, SizeHint
+from vel.module.input.image_to_tensor import image_to_tensor
+from vel.net.layer_base import LayerFactory, Layer, LayerFactoryContext, LayerInfo
+
+
+class ImageToTensorLayer(Layer):
+    """
+    Convert simple image to tensor.
+
+    Flip channels to a [C, W, H] order and potentially convert 8-bit color values to floats
+    """
+    def __init__(self, info: LayerInfo, shape: tuple = None):
+        super().__init__(info)
+
+        if shape is not None:
+            assert len(shape) == 3, "Images must have three dimensions"
+            self.w, self.h, self.c = shape
+        else:
+            self.w, self.h, self.c = (None, None, None)
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return image_to_tensor(direct)
+
+    def size_hints(self) -> SizeHints:
+        return SizeHints(SizeHint(None, self.c, self.w, self.h))
+
+
+class ImageToTensorLayerFactory(LayerFactory):
+    def __init__(self, shape: tuple = None):
+        super().__init__()
+        self.shape = shape
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "image_to_tensor"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        if self.shape is None:
+            shape = direct_input.assert_single().shape()
+        else:
+            shape = self.shape
+
+        return ImageToTensorLayer(
+            info=self.make_info(context),
+            shape=shape
+        )
+
+
+def create(shape: tuple = None, label=None, group=None):
+    """ Vel factory function """
+    return ImageToTensorLayerFactory(shape=shape).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/input/normalize_ewma.py b/vel/net/layer/input/normalize_ewma.py
new file mode 100644
index 00000000..64e1cbb3
--- /dev/null
+++ b/vel/net/layer/input/normalize_ewma.py
@@ -0,0 +1,68 @@
+from vel.api import SizeHints, SizeHint
+from vel.module.input.normalize_ewma import NormalizeEwma
+from vel.net.layer_base import LayerFactory, Layer, LayerFactoryContext, LayerInfo
+
+
+class NormalizeEwmaLayer(Layer):
+    """ Layer that normalizes the inputs """
+
+    def __init__(self, info: LayerInfo, input_shape: SizeHints, beta: float = 0.99, epsilon: float = 1e-1,
+                 per_element_update=False):
+        super().__init__(info)
+
+        self.input_shape = input_shape
+        self.beta = beta
+        self.epsilon = epsilon
+        self.per_element_update = per_element_update
+
+        self.normalize = NormalizeEwma(
+            beta=self.beta,
+            epsilon=self.epsilon,
+            per_element_update=self.per_element_update,
+            input_shape=self.input_shape.assert_single()[1:]  # Remove batch axis
+        )
+
+    def reset_weights(self):
+        self.normalize.reset_weights()
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return self.normalize(direct)
+
+    def size_hints(self) -> SizeHints:
+        return self.input_shape
+
+
+class NormalizeEwmaLayerFactory(LayerFactory):
+    def __init__(self, beta: float = 0.99, epsilon: float = 1e-2, shape=None, per_element_update=False):
+        super().__init__()
+        self.shape = shape
+        self.beta = beta
+        self.epsilon = epsilon
+        self.per_element_update = per_element_update
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "image_to_tensor"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        if self.shape is None:
+            input_shape = direct_input
+        else:
+            input_shape = SizeHints(SizeHint(*([None] + list(self.shape))))
+
+        return NormalizeEwmaLayer(
+            info=self.make_info(context),
+            beta=self.beta,
+            epsilon=self.epsilon,
+            per_element_update=self.per_element_update,
+            input_shape=input_shape
+        )
+
+
+def create(beta=0.99, epsilon=1e-1, shape=None, per_element_update=False, label=None, group=None):
+    """ Vel factory function """
+    return NormalizeEwmaLayerFactory(
+        beta=beta, epsilon=epsilon, shape=shape, per_element_update=per_element_update
+    ).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/input/normalize_expanding.py b/vel/net/layer/input/normalize_expanding.py
new file mode 100644
index 00000000..35dc2abe
--- /dev/null
+++ b/vel/net/layer/input/normalize_expanding.py
@@ -0,0 +1,50 @@
+from vel.api import SizeHints, SizeHint
+from vel.module.input.normalize_expanding import NormalizeExpanding
+from vel.net.layer_base import LayerFactory, Layer, LayerFactoryContext, LayerInfo
+
+
+class NormalizeLayer(Layer):
+    """ Layer that normalizes the inputs """
+
+    def __init__(self, info: LayerInfo, input_shape: SizeHints):
+        super().__init__(info)
+
+        self.input_shape = input_shape
+
+        self.normalize = NormalizeExpanding(
+            input_shape=self.input_shape.assert_single()[1:]  # Remove batch axis
+        )
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return self.normalize(direct)
+
+    def size_hints(self) -> SizeHints:
+        return self.input_shape
+
+
+class NormalizeLayerFactory(LayerFactory):
+    def __init__(self, shape=None):
+        super().__init__()
+        self.shape = shape
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "image_to_tensor"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        if self.shape is None:
+            input_shape = direct_input
+        else:
+            input_shape = SizeHints(SizeHint(*([None] + list(self.shape))))
+
+        return NormalizeLayer(
+            info=self.make_info(context),
+            input_shape=input_shape
+        )
+
+
+def create(shape=None):
+    """ Vel factory function """
+    return NormalizeLayerFactory(shape=shape)
diff --git a/vel/net/layer/mlp.py b/vel/net/layer/mlp.py
new file mode 100644
index 00000000..0e7d27b3
--- /dev/null
+++ b/vel/net/layer/mlp.py
@@ -0,0 +1,91 @@
+"""
+Code based loosely on the implementation:
+https://github.com/openai/baselines/blob/master/baselines/common/models.py
+
+Under MIT license.
+"""
+import typing
+
+import numpy as np
+import torch.nn as nn
+import torch.nn.init as init
+
+import vel.util.network as net_util
+
+from vel.api import SizeHints
+from vel.net.layer_base import LayerFactory, Layer, LayerInfo, LayerFactoryContext
+
+
+class MLP(Layer):
+    """ Simple Multi-Layer-Perceptron network """
+    def __init__(self, info: LayerInfo, input_shape: SizeHints, hidden_layers: typing.List[int],
+                 activation: str = 'tanh', normalization: typing.Optional[str] = None):
+        super().__init__(info)
+
+        self.input_shape = input_shape
+        self.input_length = input_shape.assert_single().last()
+        self.hidden_layers = hidden_layers
+        self.activation = activation
+        self.normalization = normalization
+
+        layer_objects = []
+        layer_sizes = zip([self.input_length] + hidden_layers, hidden_layers)
+
+        for i_size, o_size in layer_sizes:
+            layer_objects.append(nn.Linear(i_size, o_size))
+
+            if self.normalization:
+                layer_objects.append(net_util.normalization(normalization)(o_size))
+
+            layer_objects.append(net_util.activation(activation)())
+
+        self.model = nn.Sequential(*layer_objects)
+        self.hidden_units = hidden_layers[-1] if hidden_layers else self.input_length
+
+        self.output_shape = SizeHints(input_shape.assert_single().drop_last().append(self.hidden_units))
+
+    def reset_weights(self):
+        """ Call proper initializers for the weights """
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                # init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                init.orthogonal_(m.weight, gain=np.sqrt(2))
+                init.constant_(m.bias, 0.0)
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return self.model(direct.float())
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.output_shape
+
+
+class MLPFactory(LayerFactory):
+    def __init__(self, hidden_layers: typing.List[int], activation: str = 'tanh',
+                 normalization: typing.Optional[str] = None):
+        super().__init__()
+        self.hidden_layers = hidden_layers
+        self.activation = activation
+        self.normalization = normalization
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "mlp"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        return MLP(
+            info=self.make_info(context),
+            input_shape=direct_input,
+            hidden_layers=self.hidden_layers,
+            activation=self.activation,
+            normalization=self.normalization
+        )
+
+
+def create(hidden_layers: [int], activation='tanh', normalization=None, label=None, group=None):
+    """ Vel factory function """
+    return MLPFactory(
+        hidden_layers=hidden_layers, activation=activation, normalization=normalization
+    ).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/modules/__init__.py b/vel/net/layer/nlp/__init__.py
similarity index 100%
rename from vel/rl/modules/__init__.py
rename to vel/net/layer/nlp/__init__.py
diff --git a/vel/net/layer/nlp/alphabet_embedding.py b/vel/net/layer/nlp/alphabet_embedding.py
new file mode 100644
index 00000000..2e61b86d
--- /dev/null
+++ b/vel/net/layer/nlp/alphabet_embedding.py
@@ -0,0 +1,55 @@
+import torch.nn as nn
+
+from vel.api import SizeHints
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
+
+
+class AlphabetEmbeddingLayer(Layer):
+    """
+    Encode incoming tensor encoded using certain alphabet into one-hot encoding
+    """
+    def __init__(self, info: LayerInfo, alphabet_size: int, dim: int, input_shape: SizeHints):
+        super().__init__(info)
+
+        self.alphabet_size = alphabet_size
+        self.dim = dim
+        self.output_shape = SizeHints(input_shape.assert_single().append(self.dim))
+
+        self.layer = nn.Embedding(self.alphabet_size + 1, self.dim)
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        """ Forward propagation of a single layer """
+        return self.layer(direct)
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.output_shape
+
+
+class AlphabetEmbeddingLayerFactory(LayerFactory):
+    """ Factory class for the AlphabetOneHotEncode layer """
+
+    def __init__(self, dim: int):
+        super().__init__()
+        self.dim = dim
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "alphabet_embedding"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        alphabet_size = extra_args['alphabet_size']
+
+        return AlphabetEmbeddingLayer(
+            info=self.make_info(context),
+            alphabet_size=alphabet_size,
+            dim=self.dim,
+            input_shape=direct_input
+        )
+
+
+def create(dim: int, label=None, group=None):
+    """ Vel factory function """
+    return AlphabetEmbeddingLayerFactory(dim).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/nlp/alphabet_one_hot_encode.py b/vel/net/layer/nlp/alphabet_one_hot_encode.py
new file mode 100644
index 00000000..a1f27f7f
--- /dev/null
+++ b/vel/net/layer/nlp/alphabet_one_hot_encode.py
@@ -0,0 +1,53 @@
+from vel.api import SizeHints
+from vel.net.layer_base import Layer, LayerFactory, LayerInfo, LayerFactoryContext
+
+from vel.util.tensor_util import one_hot_encoding
+
+
+class AlphabetOneHotEncodeLayer(Layer):
+    """
+    Encode incoming tensor encoded using certain alphabet into one-hot encoding
+    """
+    def __init__(self, info: LayerInfo, alphabet_size: int, input_shape: SizeHints):
+        super().__init__(info)
+
+        self.alphabet_size = alphabet_size
+        self.output_shape = SizeHints(input_shape.assert_single().append(self.alphabet_size + 1))
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return one_hot_encoding(direct, num_labels=self.alphabet_size + 1)
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.output_shape
+
+
+class AlphabetOneHotEncodeLayerFactory(LayerFactory):
+    """ Factory class for the AlphabetOneHotEncode layer """
+
+    def __init__(self, alphabet_size):
+        super().__init__()
+        self.alphabet_size = alphabet_size
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "alphabet_one_hot_encode"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        if 'alphabet_size' in extra_args:
+            alphabet_size = extra_args['alphabet_size']
+        else:
+            alphabet_size = self.alphabet_size
+
+        return AlphabetOneHotEncodeLayer(
+            info=self.make_info(context),
+            alphabet_size=alphabet_size,
+            input_shape=direct_input
+        )
+
+
+def create(alphabet_size=None, label=None, group=None):
+    """ Vel factory function """
+    return AlphabetOneHotEncodeLayerFactory(alphabet_size=alphabet_size).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/nlp/pretrained_embedding.py b/vel/net/layer/nlp/pretrained_embedding.py
new file mode 100644
index 00000000..45350838
--- /dev/null
+++ b/vel/net/layer/nlp/pretrained_embedding.py
@@ -0,0 +1,61 @@
+import numpy as np
+
+import torch.nn as nn
+
+from vel.api import SizeHints, LanguageSource
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
+
+
+class PretrainedEmbeddingLayer(Layer):
+    """ Load a pretrained word embedding """
+    def __init__(self, info: LayerInfo, vectors: np.ndarray, input_shape: SizeHints, freeze: bool = False):
+        super().__init__(info)
+
+        self.output_shape = SizeHints(input_shape.assert_single().append(vectors.shape[1]))
+
+        self.layer = nn.Embedding(vectors.shape[0], vectors.shape[1])
+        self.layer.weight.data.copy_(vectors)
+
+        self.freeze = freeze
+
+        if self.freeze:
+            self.layer.weight.requires_grad_(False)
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return self.layer(direct)
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.output_shape
+
+
+class PretrainedEmbeddingLayerFactory(LayerFactory):
+    """ Load a pretrained word embedding """
+    def __init__(self, source: LanguageSource, vectors: str, freeze: bool):
+        super().__init__()
+        self.vectors = vectors
+        self.source = source
+        self.freeze = freeze
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "pretrained_embedding"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        vocab = self.source.fields[self.source.mapping['x']].vocab
+        vocab.load_vectors(self.vectors)
+
+        return PretrainedEmbeddingLayer(
+            info=self.make_info(context),
+            vectors=vocab.vectors,
+            freeze=self.freeze,
+            input_shape=direct_input,
+        )
+
+
+def create(source: LanguageSource, vectors: str, freeze: bool = False, label=None, group=None):
+    """ Vel factory function """
+    return PretrainedEmbeddingLayerFactory(
+        source, vectors, freeze=freeze
+    ).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/nlp/select_final_features.py b/vel/net/layer/nlp/select_final_features.py
new file mode 100644
index 00000000..4b55b303
--- /dev/null
+++ b/vel/net/layer/nlp/select_final_features.py
@@ -0,0 +1,65 @@
+import torch
+
+from vel.api import SizeHints, SizeHint
+from vel.net.layer_base import Layer, LayerFactory, LayerInfo, LayerFactoryContext
+
+
+class SelectFinalFeaturesLayer(Layer):
+    """
+    For many sequence processing tasks we only care about the output from the final element
+    """
+    def __init__(self, info: LayerInfo, bidirectional: bool, input_shape: SizeHints):
+        super().__init__(info)
+
+        self.bidirectional = bidirectional
+
+        b, s, x = input_shape.assert_single(3)
+        self.output_shape = SizeHints(SizeHint(b, x))
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        if self.bidirectional:
+            final_shape = direct.shape[-1]
+            assert final_shape % 2 == 0
+            half_final_shape = final_shape // 2
+
+            # dimensions are: batch, seq, features
+            # first one is from forward pass
+            # second one is backward pass
+            part1 = direct[:, -1, :half_final_shape]
+            part2 = direct[:, 0, half_final_shape:]
+
+            return torch.cat([part1, part2], dim=1)
+        else:
+            return direct[:, -1, :]
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.output_shape
+
+
+class SelectFinalFeaturesLayerFactory(LayerFactory):
+    """ Factory for the SelectFinalFeatures layer """
+
+    def __init__(self, bidirectional: bool = False):
+        super().__init__()
+        self.bidirectional = bidirectional
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "select_final_features"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        return SelectFinalFeaturesLayer(
+            info=self.make_info(context),
+            bidirectional=self.bidirectional,
+            input_shape=direct_input
+        )
+
+
+def create(bidirectional=False, label=None, group=None):
+    """ Vel factory function """
+    return SelectFinalFeaturesLayerFactory(
+        bidirectional=bidirectional
+    ).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/modules/noise/__init__.py b/vel/net/layer/residual/__init__.py
similarity index 100%
rename from vel/rl/modules/noise/__init__.py
rename to vel/net/layer/residual/__init__.py
diff --git a/vel/net/layer/residual/fc_res.py b/vel/net/layer/residual/fc_res.py
new file mode 100644
index 00000000..393dd5e3
--- /dev/null
+++ b/vel/net/layer/residual/fc_res.py
@@ -0,0 +1,101 @@
+import typing
+import torch.nn as nn
+import torch.nn.init as init
+import numpy as np
+
+from vel.api import SizeHint, SizeHints
+from vel.net.layer_base import Layer, LayerFactory, LayerInfo, LayerFactoryContext
+
+
+class FcResidual(Layer):
+    """ Residual fully-connected block """
+
+    def __init__(self, info: LayerInfo, input_shape: SizeHint, divisor: int = 1, activation: str = 'relu',
+                 normalization: typing.Optional[str] = None):
+        super().__init__(info)
+
+        self._size_hints = SizeHints(input_shape)
+
+        self.trunk_shape = input_shape[-1]
+        self.bottleneck_shape = self.trunk_shape // divisor
+
+        self.f1 = nn.Linear(self.trunk_shape, self.bottleneck_shape)
+
+        if normalization == 'layer':
+            self.n1 = nn.LayerNorm(self.bottleneck_shape)
+        elif normalization is None:
+            self.n1 = nn.Identity()
+        else:
+            raise NotImplementedError
+
+        if activation == 'relu':
+            self.a1 = nn.ReLU(inplace=True)
+        else:
+            raise NotImplementedError
+
+        self.f2 = nn.Linear(self.bottleneck_shape, self.trunk_shape)
+
+        if normalization == 'layer':
+            self.n2 = nn.LayerNorm(self.trunk_shape)
+        elif normalization is None:
+            self.n2 = nn.Identity()
+        else:
+            raise NotImplementedError
+
+        if activation == 'relu':
+            self.a2 = nn.ReLU(inplace=True)
+        else:
+            raise NotImplementedError
+
+    def reset_weights(self):
+        """ Call proper initializers for the weights """
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                # init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                init.orthogonal_(m.weight, gain=np.sqrt(2))
+                init.constant_(m.bias, 0.0)
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self._size_hints
+
+    def forward(self, direct, state: dict, context: dict):
+        residual = direct
+
+        residual = self.a1(self.n1(self.f1(residual)))
+        residual = self.a2(self.n2(self.f2(residual)))
+
+        return residual + direct
+
+
+class FcResidualFactory(LayerFactory):
+    """ Factory for fully-connected residual layers """
+    def __init__(self, divisor: int, activation: str, normalization: typing.Optional[str] = None):
+        super().__init__()
+        self.divisor = divisor
+        self.activation = activation
+        self.normalization = normalization
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "fc_residual"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        size_hint = direct_input.assert_single()
+        info = self.make_info(context)
+
+        return FcResidual(
+            info=info,
+            input_shape=size_hint,
+            divisor=self.divisor,
+            activation=self.activation,
+            normalization=self.normalization
+        )
+
+
+def create(divisor: int, activation: str = 'relu', normalization: typing.Optional[str] = None,
+           label=None, group=None):
+    return FcResidualFactory(divisor, activation, normalization)
+
diff --git a/vel/net/layer/rnn.py b/vel/net/layer/rnn.py
new file mode 100644
index 00000000..bceb9f3d
--- /dev/null
+++ b/vel/net/layer/rnn.py
@@ -0,0 +1,133 @@
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+
+from vel.api import SizeHints
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
+
+
+class RnnLayer(Layer):
+    """ Single Recurrent Layer """
+    def __init__(self, info: LayerInfo, input_shape: SizeHints, hidden_size: int, rnn_type: str,
+                 bias: bool = True, bidirectional: bool = False, nonlinearity: str = 'tanh'):
+        super().__init__(info)
+
+        self.input_shape = input_shape
+        self.input_length = input_shape.assert_single().last()
+        self.hidden_size = hidden_size
+        self.rnn_type = rnn_type
+
+        self.bias = bias
+        self.bidirectional = bidirectional
+        self.nonlinearity = nonlinearity
+
+        if self.rnn_type == 'rnn':
+            self.rnn_cell = nn.RNN(
+                input_size=self.input_length, hidden_size=hidden_size, bias=bias, nonlinearity=nonlinearity,
+                bidirectional=bidirectional, batch_first=True
+            )
+        elif self.rnn_type == 'lstm':
+            self.rnn_cell = nn.LSTM(
+                input_size=self.input_length, hidden_size=hidden_size, bias=bias,
+                bidirectional=bidirectional, batch_first=True
+            )
+        elif self.rnn_type == 'gru':
+            self.rnn_cell = nn.GRU(
+                input_size=self.input_length, hidden_size=hidden_size, bias=bias,
+                bidirectional=bidirectional, batch_first=True
+            )
+
+        if self.bidirectional:
+            self.output_shape = SizeHints(input_shape.assert_single().drop_last().append(2 * self.hidden_size))
+        else:
+            self.output_shape = SizeHints(input_shape.assert_single().drop_last().append(self.hidden_size))
+
+    def reset_weights(self):
+        """ Call proper initializers for the weights """
+        init.xavier_normal_(self.rnn_cell.weight_hh)
+        init.xavier_normal_(self.rnn_cell.weight_ih)
+        init.zeros_(self.rnn_cell.bias_ih)
+        init.zeros_(self.rnn_cell.bias_hh)
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return True
+
+    @property
+    def state_dim(self) -> int:
+        """ Dimension of model state """
+        if self.rnn_type == 'lstm':
+            return 2 * self.hidden_size
+        else:
+            return self.hidden_size
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        if self.bidirectional:
+            return {self.global_name: torch.zeros(2, batch_size, self.state_dim)}
+        else:
+            return {self.global_name: torch.zeros(1, batch_size, self.state_dim)}
+
+    def forward(self, input_data, state: dict, context: dict = None):
+        """ Forward propagation of a single layer """
+
+        if self.rnn_type == 'lstm':
+            state_tensor = state[self.name]
+            hidden_state, cell_state = torch.split(state_tensor, self.hidden_size, dim=2)
+            output, (hidden_state, cell_state) = self.rnn_cell(
+                input_data, (hidden_state.contiguous(), cell_state.contiguous())
+            )
+            new_state = torch.cat([hidden_state, cell_state], dim=2)
+            return output, {self.name: new_state}
+        else:
+            state_tensor = state[self.name]
+            output, new_state = self.rnn_cell(input_data, state_tensor)
+            return output, {self.name: new_state}
+
+    def size_hints(self) -> SizeHints:
+        """ Size hints for this network """
+        return self.output_shape
+
+
+class RnnLayerFactory(LayerFactory):
+    """ Factory class for the RnnLayer """
+
+    def __init__(self, hidden_size: int, rnn_type: str, bias: bool = True, bidirectional: bool = False,
+                 nonlinearity: str = 'tanh'):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.rnn_type = rnn_type
+
+        self.bias = bias
+        self.bidirectional = bidirectional
+        self.nonlinearity = nonlinearity
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "rnn"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create instance of 'RnnLayer' """
+        return RnnLayer(
+            info=self.make_info(context),
+            input_shape=direct_input,
+            hidden_size=self.hidden_size,
+            rnn_type=self.rnn_type,
+            bias=self.bias,
+            bidirectional=self.bidirectional,
+            nonlinearity=self.nonlinearity
+        )
+
+
+def create(hidden_size: int, rnn_type: str, bias: bool = True, bidirectional: bool = False,
+           nonlinearity: str = 'tanh', label=None, group=None):
+    """ Vel factory function """
+    return RnnLayerFactory(
+        hidden_size=hidden_size,
+        rnn_type=rnn_type,
+        bias=bias,
+        bidirectional=bidirectional,
+        nonlinearity=nonlinearity
+    ).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/modules/test/__init__.py b/vel/net/layer/util/__init__.py
similarity index 100%
rename from vel/rl/modules/test/__init__.py
rename to vel/net/layer/util/__init__.py
diff --git a/vel/net/layer/util/concat.py b/vel/net/layer/util/concat.py
new file mode 100644
index 00000000..511b8c66
--- /dev/null
+++ b/vel/net/layer/util/concat.py
@@ -0,0 +1,62 @@
+import torch
+
+from vel.api import SizeHints, SizeHint
+from vel.net.layer_base import LayerFactory, Layer, LayerFactoryContext, LayerInfo
+
+
+class Concat(Layer):
+    """ Repeat single tensor multiple times """
+
+    def __init__(self, info: LayerInfo, size_hints: SizeHints, axis: int = -1):
+        super().__init__(info)
+
+        self.axis = axis
+        self._size_hints = size_hints
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return torch.cat(direct, dim=self.axis)
+
+    def size_hints(self) -> SizeHints:
+        return self._size_hints
+
+
+class ConcatFactory(LayerFactory):
+    """ Factory for Concat Layer """
+    def __init__(self, axis: int = -1):
+        super().__init__()
+        self.axis = axis
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "concat"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        inputs = direct_input.assert_tuple()
+
+        result = []
+        dimension = len(inputs[0])
+
+        for i in range(dimension):
+
+            if i == (self.axis % dimension):
+                candidates = [el[i] for el in inputs]
+
+                if None in candidates:
+                    result.append(None)
+                else:
+                    result.append(sum(candidates))
+            else:
+                result.append(inputs[0][i])
+
+        return Concat(
+            info=self.make_info(context),
+            axis=self.axis,
+            size_hints=SizeHints(SizeHint(*result))
+        )
+
+
+def create(axis: int = -1, label=None, group=None):
+    """ Vel factory function """
+    return ConcatFactory(axis=axis).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/util/flatten.py b/vel/net/layer/util/flatten.py
new file mode 100644
index 00000000..3b4a9cfd
--- /dev/null
+++ b/vel/net/layer/util/flatten.py
@@ -0,0 +1,39 @@
+import numpy as np
+
+from vel.api import SizeHints, SizeHint
+from vel.net.layer_base import LayerFactory, Layer, LayerFactoryContext, LayerInfo
+
+
+class Flatten(Layer):
+    """ Flatten single tensor to a unit shape """
+
+    def __init__(self, info: LayerInfo, size_hint: SizeHint):
+        super().__init__(info)
+
+        self._size_hints = SizeHints(SizeHint(None, np.prod(size_hint[1:])))
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return direct.view(direct.size(0), -1)
+
+    def size_hints(self) -> SizeHints:
+        return self._size_hints
+
+
+class FlattenFactory(LayerFactory):
+    """ Factory for Concat Layer """
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "flatten"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        return Flatten(
+            info=self.make_info(context),
+            size_hint=direct_input.assert_single()
+        )
+
+
+def create(label=None, group=None):
+    """ Vel factory function """
+    return FlattenFactory().with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer/util/repeat.py b/vel/net/layer/util/repeat.py
new file mode 100644
index 00000000..8041ebde
--- /dev/null
+++ b/vel/net/layer/util/repeat.py
@@ -0,0 +1,41 @@
+from vel.api import SizeHints, SizeHint
+from vel.net.layer_base import LayerFactory, Layer, LayerInfo, LayerFactoryContext
+
+
+class RepeatTensor(Layer):
+    """ Repeat single tensor multiple times """
+
+    def __init__(self, info: LayerInfo, times: int, size_hint: SizeHint):
+        super().__init__(info)
+
+        self.times = times
+        self.size_hint = size_hint
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        return tuple([direct] * self.times)
+
+    def size_hints(self) -> SizeHints:
+        return SizeHints(tuple([self.size_hint] * self.times))
+
+
+class RepeatTensorFactory(LayerFactory):
+    def __init__(self, times: int):
+        super().__init__()
+        self.times = times
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "repeat_tensor"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        return RepeatTensor(
+            info=self.make_info(context),
+            times=self.times,
+            size_hint=direct_input.assert_single()
+        )
+
+
+def create(times: int, label=None, group=None):
+    """ Vel factory function """
+    return RepeatTensorFactory(times=times).with_given_name(label).with_given_group(group)
diff --git a/vel/net/layer_base.py b/vel/net/layer_base.py
new file mode 100644
index 00000000..e974a8f1
--- /dev/null
+++ b/vel/net/layer_base.py
@@ -0,0 +1,113 @@
+import attr
+import typing
+
+from vel.api import BackboneModule, SizeHints
+
+
+@attr.s(auto_attribs=True)
+class LayerInfo:
+    """ Information about the layer """
+    name: str
+    global_name: str
+    group: str
+
+
+class Layer(BackboneModule):
+    """ Layer class that fits into modular network framework """
+    def __init__(self, info: LayerInfo):
+        super().__init__()
+
+        self.info = info
+
+    @property
+    def name(self) -> str:
+        """ Name of this layer """
+        return self.info.name
+
+    @property
+    def global_name(self) -> str:
+        """ Name of this layer - globally unique version """
+        return self.info.global_name
+
+    @property
+    def group(self) -> str:
+        """ Group of this layer """
+        return self.info.group
+
+    def forward(self, direct, state: dict, context: dict):
+        """ Forward propagation of a single layer """
+        raise NotImplementedError
+
+    def grouped_parameters(self) -> typing.Iterable[typing.Tuple[str, object]]:
+        """ Return iterable of pairs (group, parameters) """
+        return [(self.group, self.parameters())]
+
+
+@attr.s(auto_attribs=True)
+class LayerFactoryContext:
+    """ Context information about the layer being currently created """
+
+    idx: int
+    """ Index of this layer within parent """
+
+    parent_group: str
+    """ Group of the parent layer """
+
+    parent_name: typing.Optional[str] = None
+    """ Name of the parent - None if it's a top level layer """
+
+    data: dict = {}
+    """ Generic information potentially passed by layer in a hierarchy """
+
+
+class LayerFactory:
+    """ Factory for layers """
+    def __init__(self):
+        self.given_name = None
+        self.given_group = None
+
+    def with_given_name(self, given_name) -> 'LayerFactory':
+        """ Set given name """
+        self.given_name = given_name
+        return self
+
+    def with_given_group(self, given_group) -> 'LayerFactory':
+        """ Set given group """
+        self.given_group = given_group
+        return self
+
+    def suggested_name(self, idx: int):
+        """ Reasonable layer name suggestion """
+        return "{}_{:04d}".format(self.name_base, idx)
+
+    def make_info(self, context: LayerFactoryContext) -> LayerInfo:
+        """ Make info for child layer """
+        if self.given_name is not None:
+            name = self.given_name
+        else:
+            name = self.suggested_name(context.idx)
+
+        if self.given_group is not None:
+            group = self.given_group
+        else:
+            group = context.parent_group
+
+        if context.parent_name is None:
+            global_name = name
+        else:
+            global_name = f"{context.parent_name}/{name}"
+
+        return LayerInfo(
+            name=name,
+            group=group,
+            global_name=global_name
+        )
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        raise NotImplementedError
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        raise NotImplementedError
diff --git a/vel/net/layer_list.py b/vel/net/layer_list.py
new file mode 100644
index 00000000..24d44e7d
--- /dev/null
+++ b/vel/net/layer_list.py
@@ -0,0 +1,65 @@
+import collections
+
+from vel.api import BackboneModule, SizeHints
+
+
+class LayerList(BackboneModule):
+    """ Modification of nn.Sequential for the purpose of modular networks """
+    def __init__(self, layers: collections.OrderedDict):
+        super().__init__()
+
+        self._layers = []
+
+        for key, module in layers.items():
+            self.add_module(key, module)
+            self._layers.append(module)
+
+        self._is_stateful = any(l.is_stateful for l in self._layers)
+
+    def reset_weights(self):
+        for l in self._layers:
+            l.reset_weights()
+
+    def size_hints(self) -> SizeHints:
+        return self._layers[-1].size_hints()
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return self._is_stateful
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        zero_state = {}
+
+        for l in self._layers:
+            if l.is_stateful:
+                layer_zero_state = l.zero_state(batch_size)
+                if layer_zero_state is not None:
+                    zero_state.update(layer_zero_state)
+
+        return zero_state
+
+    def __len__(self):
+        return len(self._layers)
+
+    def __getitem__(self, item):
+        return self._layers[item]
+
+    def forward(self, direct, state: dict = None, context: dict = None):
+        if not self._is_stateful:
+            for layer in self._layers:
+                direct = layer(direct, state=state, context=context)
+            return direct
+        else:
+            data = direct
+            output_state = {}
+
+            for layer in self._layers:
+                if layer.is_stateful:
+                    data, new_state = layer(data, state=state, context=context)
+                    output_state.update(new_state)
+                else:
+                    data = layer(data, state=state, context=context)
+
+            return data, output_state
diff --git a/vel/net/modular.py b/vel/net/modular.py
new file mode 100644
index 00000000..82573056
--- /dev/null
+++ b/vel/net/modular.py
@@ -0,0 +1,144 @@
+import itertools as it
+import collections
+
+import torch.nn as nn
+
+from vel.api import BackboneModule, ModuleFactory, SizeHints
+from vel.util.tensor_util import to_device
+
+from .layer_base import LayerFactory, LayerFactoryContext
+from .layer_list import LayerList
+
+
+def instantiate_layers(layers: [LayerFactory], group: str, size_hint: SizeHints, extra_args: dict) -> nn.Module:
+    """ Instantiate list of layer factories into PyTorch Module """
+    module_dict = collections.OrderedDict()
+    context_data = {}
+
+    for idx, layer_factory in enumerate(layers):
+        counter = idx + 1
+
+        context = LayerFactoryContext(
+            idx=counter,
+            parent_group=group,
+            parent_name=None,
+            data=context_data
+        )
+
+        layer = layer_factory.instantiate(direct_input=size_hint, context=context, extra_args=extra_args)
+        size_hint = layer.size_hints()
+
+        module_dict[layer.name] = layer
+
+    return LayerList(module_dict)
+
+
+class ModularNetwork(BackboneModule):
+    """ Network that is built from layers """
+
+    def __init__(self, layers: LayerList):
+        super().__init__()
+
+        self.layers = layers
+        assert not self.layers.is_stateful
+
+    def reset_weights(self):
+        """ Call proper initializers for the weights """
+        self.layers.reset_weights()
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return False
+
+    def size_hints(self) -> SizeHints:
+        return self.layers.size_hints()
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        return None
+
+    def reset_state(self, state, dones):
+        """ Reset the state after the episode has been terminated """
+        raise NotImplementedError
+
+    def forward(self, input_data, state=None, context: dict = None):
+        return self.layers(input_data, state=None, context=context)
+
+    def grouped_parameters(self):
+        """ Return iterable of pairs (group, parameters) """
+        return it.chain.from_iterable(l.grouped_parameters() for l in self.layers)
+
+
+class StatefulModularNetwork(BackboneModule):
+    """ Modular network handling the state between the episodes """
+
+    def __init__(self, layers: LayerList):
+        super().__init__()
+
+        self.layers = layers
+
+    def reset_weights(self):
+        """ Call proper initializers for the weights """
+        self.layers.reset_weights()
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return True
+
+    def size_hints(self) -> SizeHints:
+        return self.layers.size_hints()
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        return self.layers.zero_state(batch_size)
+
+    def reset_state(self, state, dones):
+        """ Reset the state after the episode has been terminated """
+        raise NotImplementedError
+
+    def forward(self, input_data, state=None):
+        data = input_data
+        context = {}
+
+        if state is None:
+            # input_data.device here may break. Should be fixed at some point
+            state = to_device(self.zero_state(input_data.size(0)), input_data.device)
+
+        data, output_state = self.layers(data, state=state, context=context)
+
+        return data, output_state
+
+    def grouped_parameters(self):
+        """ Return iterable of pairs (group, parameters) """
+        return it.chain.from_iterable(l.grouped_parameters() for l in self.layers)
+
+
+class ModularNetworkFactory(ModuleFactory):
+    """ Factory class for the modular network """
+    def __init__(self, layers: [LayerFactory], group=None):
+        self.layers = layers
+
+        if group is None:
+            self.group = "default"
+        else:
+            self.group = group
+
+    def instantiate(self, size_hint=None, **extra_args) -> BackboneModule:
+        """ Create either stateful or not modular network instance """
+        if size_hint is None:
+            size_hint = SizeHints()
+
+        layers = instantiate_layers(self.layers, self.group, size_hint=size_hint, extra_args=extra_args)
+        is_stateful = any(l.is_stateful for l in layers)
+
+        if is_stateful:
+            return StatefulModularNetwork(layers)
+        else:
+            return ModularNetwork(layers)
+
+
+def create(layers: [LayerFactory], group=None):
+    """ Vel factory function """
+    return ModularNetworkFactory(layers, group)
diff --git a/vel/notebook/__init__.py b/vel/notebook/__init__.py
index 5d173dba..a8655a0b 100644
--- a/vel/notebook/__init__.py
+++ b/vel/notebook/__init__.py
@@ -1 +1,2 @@
-from .loader import load
\ No newline at end of file
+from .loader import load_config, script  # noqa
+from .defaults import reasonable_notbook_defaults  # noqa
diff --git a/vel/notebook/defaults.py b/vel/notebook/defaults.py
new file mode 100644
index 00000000..3781ad0b
--- /dev/null
+++ b/vel/notebook/defaults.py
@@ -0,0 +1,6 @@
+
+
+def reasonable_notbook_defaults():
+    """ Notbook defaults """
+    import matplotlib.pyplot as plt
+    plt.rcParams['figure.figsize'] = [15, 8]
diff --git a/vel/notebook/loader.py b/vel/notebook/loader.py
index 9edc5e1d..5efbd88c 100644
--- a/vel/notebook/loader.py
+++ b/vel/notebook/loader.py
@@ -1,8 +1,21 @@
 from vel.api import ModelConfig
 
 
-def load(config_path, run_number=0, device='cuda:0'):
+def load_config(config_path, run_number=0, device='cuda:0', resume_training=True):
     """ Load a ModelConfig from filename """
-    model_config = ModelConfig.from_file(config_path, run_number, device=device)
+    return ModelConfig.from_file(
+        ModelConfig.from_project_directory(config_path),
+        run_number=run_number,
+        device=device,
+        resume_training=resume_training
+    )
 
-    return model_config
+
+def script(model_name: str = 'script', run_number=0, device='cuda:0', resume_training=True):
+    """ Create an ad-hoc script model config """
+    return ModelConfig.script(
+        model_name=model_name,
+        run_number=run_number,
+        device=device,
+        resume_training=resume_training
+    )
diff --git a/vel/openai/baselines/common/atari_wrappers.py b/vel/openai/baselines/common/atari_wrappers.py
index a5a7fa9a..3b3540b7 100644
--- a/vel/openai/baselines/common/atari_wrappers.py
+++ b/vel/openai/baselines/common/atari_wrappers.py
@@ -4,6 +4,8 @@
 from gym import spaces
 import cv2
 cv2.ocl.setUseOpenCL(False)
+from .wrappers import TimeLimit
+
 
 class NoopResetEnv(gym.Wrapper):
     def __init__(self, env, noop_max=30):
@@ -174,27 +176,60 @@ def reward(self, reward):
         """Bin reward to {+1, 0, -1} by its sign."""
         return np.sign(reward)
 
+
 class WarpFrame(gym.ObservationWrapper):
-    def __init__(self, env, width=84, height=84, grayscale=True):
-        """Warp frames to 84x84 as done in the Nature paper and later work."""
-        gym.ObservationWrapper.__init__(self, env)
-        self.width = width
-        self.height = height
-        self.grayscale = grayscale
-        if self.grayscale:
-            self.observation_space = spaces.Box(low=0, high=255,
-                shape=(self.height, self.width, 1), dtype=np.uint8)
+    def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None):
+        """
+        Warp frames to 84x84 as done in the Nature paper and later work.
+
+        If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
+        observation should be warped.
+        """
+        super().__init__(env)
+        self._width = width
+        self._height = height
+        self._grayscale = grayscale
+        self._key = dict_space_key
+        if self._grayscale:
+            num_colors = 1
+        else:
+            num_colors = 3
+
+        new_space = gym.spaces.Box(
+            low=0,
+            high=255,
+            shape=(self._height, self._width, num_colors),
+            dtype=np.uint8,
+        )
+        if self._key is None:
+            original_space = self.observation_space
+            self.observation_space = new_space
         else:
-            self.observation_space = spaces.Box(low=0, high=255,
-                shape=(self.height, self.width, 3), dtype=np.uint8)
+            original_space = self.observation_space.spaces[self._key]
+            self.observation_space.spaces[self._key] = new_space
+        assert original_space.dtype == np.uint8 and len(original_space.shape) == 3
 
-    def observation(self, frame):
-        if self.grayscale:
+    def observation(self, obs):
+        if self._key is None:
+            frame = obs
+        else:
+            frame = obs[self._key]
+
+        if self._grayscale:
             frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-        frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
-        if self.grayscale:
+        frame = cv2.resize(
+            frame, (self._width, self._height), interpolation=cv2.INTER_AREA
+        )
+        if self._grayscale:
             frame = np.expand_dims(frame, -1)
-        return frame
+
+        if self._key is None:
+            obs = frame
+        else:
+            obs = obs.copy()
+            obs[self._key] = frame
+        return obs
+
 
 class FrameStack(gym.Wrapper):
     def __init__(self, env, k):
@@ -265,16 +300,15 @@ def __len__(self):
         return len(self._force())
 
     def __getitem__(self, i):
-        return self._force()[i]
+        return self._force()[..., i]
 
-def make_atari(env_id, timelimit=True):
-    # XXX(john): remove timelimit argument after gym is upgraded to allow double wrapping
+def make_atari(env_id, max_episode_steps=None):
     env = gym.make(env_id)
-    if not timelimit:
-        env = env.env
     assert 'NoFrameskip' in env.spec.id
     env = NoopResetEnv(env, noop_max=30)
     env = MaxAndSkipEnv(env, skip=4)
+    if max_episode_steps is not None:
+        env = TimeLimit(env, max_episode_steps=max_episode_steps)
     return env
 
 def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
diff --git a/vel/openai/baselines/common/retro_wrappers.py b/vel/openai/baselines/common/retro_wrappers.py
index 6e8fe912..badbbdd6 100644
--- a/vel/openai/baselines/common/retro_wrappers.py
+++ b/vel/openai/baselines/common/retro_wrappers.py
@@ -1,28 +1,12 @@
- # flake8: noqa F403, F405
-from .atari_wrappers import *
+from collections import deque
+import cv2
+cv2.ocl.setUseOpenCL(False)
+from .atari_wrappers import WarpFrame, ClipRewardEnv, FrameStack, ScaledFloatFrame
+from .wrappers import TimeLimit
 import numpy as np
 import gym
 
 
-class TimeLimit(gym.Wrapper):
-    def __init__(self, env, max_episode_steps=None):
-        super(TimeLimit, self).__init__(env)
-        self._max_episode_steps = max_episode_steps
-        self._elapsed_steps = 0
-
-    def step(self, ac):
-        observation, reward, done, info = self.env.step(ac)
-        self._elapsed_steps += 1
-        if self._elapsed_steps >= self._max_episode_steps:
-            done = True
-            info['TimeLimit.truncated'] = True
-        return observation, reward, done, info
-
-    def reset(self, **kwargs):
-        self._elapsed_steps = 0
-        return self.env.reset(**kwargs)
-
-
 class StochasticFrameSkip(gym.Wrapper):
     def __init__(self, env, n, stickprob):
         gym.Wrapper.__init__(self, env)
@@ -61,7 +45,6 @@ def step(self, ac):
     def seed(self, s):
         self.rng.seed(s)
 
-
 class PartialFrameStack(gym.Wrapper):
     def __init__(self, env, k, channel=1):
         """
@@ -71,8 +54,8 @@ def __init__(self, env, k, channel=1):
         shp = env.observation_space.shape
         self.channel = channel
         self.observation_space = gym.spaces.Box(low=0, high=255,
-                                                shape=(shp[0], shp[1], shp[2] + k - 1),
-                                                dtype=env.observation_space.dtype)
+            shape=(shp[0], shp[1], shp[2] + k - 1),
+            dtype=env.observation_space.dtype)
         self.k = k
         self.frames = deque([], maxlen=k)
         shp = env.observation_space.shape
@@ -92,8 +75,7 @@ def step(self, ac):
     def _get_ob(self):
         assert len(self.frames) == self.k
         return np.concatenate([frame if i==self.k-1 else frame[:,:,self.channel:self.channel+1]
-                               for (i, frame) in enumerate(self.frames)], axis=2)
-
+            for (i, frame) in enumerate(self.frames)], axis=2)
 
 class Downsample(gym.ObservationWrapper):
     def __init__(self, env, ratio):
@@ -103,10 +85,8 @@ def __init__(self, env, ratio):
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, oldc) = env.observation_space.shape
         newshape = (oldh//ratio, oldw//ratio, oldc)
-        self.observation_space = spaces.Box(
-            low=0, high=255,
-            shape=newshape, dtype=np.uint8
-        )
+        self.observation_space = gym.spaces.Box(low=0, high=255,
+            shape=newshape, dtype=np.uint8)
 
     def observation(self, frame):
         height, width, _ = self.observation_space.shape
@@ -115,7 +95,6 @@ def observation(self, frame):
             frame = frame[:,:,None]
         return frame
 
-
 class Rgb2gray(gym.ObservationWrapper):
     def __init__(self, env):
         """
@@ -123,10 +102,8 @@ def __init__(self, env):
         """
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, _oldc) = env.observation_space.shape
-        self.observation_space = spaces.Box(
-            low=0, high=255,
-            shape=(oldh, oldw, 1), dtype=np.uint8
-        )
+        self.observation_space = gym.spaces.Box(low=0, high=255,
+            shape=(oldh, oldw, 1), dtype=np.uint8)
 
     def observation(self, frame):
         frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
@@ -148,7 +125,6 @@ def reset(self):
         self.epcount += 1
         return self.env.reset()
 
-
 class AppendTimeout(gym.Wrapper):
     def __init__(self, env):
         gym.Wrapper.__init__(self, env)
@@ -165,7 +141,7 @@ def __init__(self, env):
             self.observation_space = gym.spaces.Dict({
                 'original': self.original_os,
                 'value_estimation_timeout': self.timeout_space
-            })
+                })
             self.dict_mode = False
         self.ac_count = None
         while 1:
@@ -191,7 +167,6 @@ def _process(self, ob):
         else:
             return { 'original': ob, 'value_estimation_timeout': fracmissing }
 
-
 class StartDoingRandomActionsWrapper(gym.Wrapper):
     """
     Warning: can eat info dicts, not good if you depend on them
@@ -224,28 +199,28 @@ def step(self, a):
                 self.some_random_steps()
         return self.last_obs, rew, done, info
 
-
-def make_retro(*, game, state, max_episode_steps, **kwargs):
+def make_retro(*, game, state=None, max_episode_steps=4500, **kwargs):
     import retro
+    if state is None:
+        state = retro.State.DEFAULT
     env = retro.make(game, state, **kwargs)
     env = StochasticFrameSkip(env, n=4, stickprob=0.25)
     if max_episode_steps is not None:
         env = TimeLimit(env, max_episode_steps=max_episode_steps)
     return env
 
-
 def wrap_deepmind_retro(env, scale=True, frame_stack=4):
     """
     Configure environment for retro games, using config similar to DeepMind-style Atari in wrap_deepmind
     """
     env = WarpFrame(env)
     env = ClipRewardEnv(env)
-    env = FrameStack(env, frame_stack)
+    if frame_stack > 1:
+        env = FrameStack(env, frame_stack)
     if scale:
         env = ScaledFloatFrame(env)
     return env
 
-
 class SonicDiscretizer(gym.ActionWrapper):
     """
     Wrap a gym-retro environment and make it use discrete
@@ -267,7 +242,6 @@ def __init__(self, env):
     def action(self, a): # pylint: disable=W0221
         return self._actions[a].copy()
 
-
 class RewardScaler(gym.RewardWrapper):
     """
     Bring rewards to a reasonable scale for PPO.
@@ -281,7 +255,6 @@ def __init__(self, env, scale=0.01):
     def reward(self, reward):
         return reward * self.scale
 
-
 class AllowBacktracking(gym.Wrapper):
     """
     Use deltas in max(X) as the reward, rather than deltas
diff --git a/vel/openai/baselines/common/running_mean_std.py b/vel/openai/baselines/common/running_mean_std.py
index a8daae81..fb891589 100644
--- a/vel/openai/baselines/common/running_mean_std.py
+++ b/vel/openai/baselines/common/running_mean_std.py
@@ -15,18 +15,20 @@ def update(self, x):
         self.update_from_moments(batch_mean, batch_var, batch_count)
 
     def update_from_moments(self, batch_mean, batch_var, batch_count):
-        delta = batch_mean - self.mean
-        tot_count = self.count + batch_count
+        self.mean, self.var, self.count = update_mean_var_count_from_moments(
+            self.mean, self.var, self.count, batch_mean, batch_var, batch_count)
 
-        new_mean = self.mean + delta * batch_count / tot_count
-        m_a = self.var * self.count
-        m_b = batch_var * batch_count
-        M2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)
-        new_var = M2 / (self.count + batch_count)
 
-        new_count = batch_count + self.count
+def update_mean_var_count_from_moments(mean, var, count, batch_mean, batch_var, batch_count):
+    delta = batch_mean - mean
+    tot_count = count + batch_count
 
-        self.mean = new_mean
-        self.var = new_var
-        self.count = new_count
+    new_mean = mean + delta * batch_count / tot_count
+    m_a = var * count
+    m_b = batch_var * batch_count
+    M2 = m_a + m_b + np.square(delta) * count * batch_count / tot_count
+    new_var = M2 / tot_count
+    new_count = tot_count
+
+    return new_mean, new_var, new_count
 
diff --git a/vel/openai/baselines/common/vec_env/__init__.py b/vel/openai/baselines/common/vec_env/__init__.py
index 0a124cb9..a0d5a348 100644
--- a/vel/openai/baselines/common/vec_env/__init__.py
+++ b/vel/openai/baselines/common/vec_env/__init__.py
@@ -1,4 +1,7 @@
+import contextlib
+import os
 from abc import ABC, abstractmethod
+
 from vel.openai.baselines.common.tile_images import tile_images
 
 
@@ -135,7 +138,6 @@ def get_viewer(self):
             self.viewer = rendering.SimpleImageViewer()
         return self.viewer
 
-
 class VecEnvWrapper(VecEnv):
     """
     An environment wrapper that applies to an entire batch
@@ -144,8 +146,7 @@ class VecEnvWrapper(VecEnv):
 
     def __init__(self, venv, observation_space=None, action_space=None):
         self.venv = venv
-        VecEnv.__init__(self,
-                        num_envs=venv.num_envs,
+        super().__init__(num_envs=venv.num_envs,
                         observation_space=observation_space or venv.observation_space,
                         action_space=action_space or venv.action_space)
 
@@ -169,6 +170,25 @@ def render(self, mode='human'):
     def get_images(self):
         return self.venv.get_images()
 
+    def __getattr__(self, name):
+        if name.startswith('_'):
+            raise AttributeError("attempted to get missing private attribute '{}'".format(name))
+        return getattr(self.venv, name)
+
+
+class VecEnvObservationWrapper(VecEnvWrapper):
+    @abstractmethod
+    def process(self, obs):
+        pass
+
+    def reset(self):
+        obs = self.venv.reset()
+        return self.process(obs)
+
+    def step_wait(self):
+        obs, rews, dones, infos = self.venv.step_wait()
+        return self.process(obs), rews, dones, infos
+
 
 class CloudpickleWrapper(object):
     """
@@ -185,3 +205,22 @@ def __getstate__(self):
     def __setstate__(self, ob):
         import pickle
         self.x = pickle.loads(ob)
+
+
+@contextlib.contextmanager
+def clear_mpi_env_vars():
+    """
+    from mpi4py import MPI will call MPI_Init by default.  If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
+    This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
+    Processes.
+    """
+    removed_environment = {}
+    for k, v in list(os.environ.items()):
+        for prefix in ['OMPI_', 'PMI_']:
+            if k.startswith(prefix):
+                removed_environment[k] = v
+                del os.environ[k]
+    try:
+        yield
+    finally:
+        os.environ.update(removed_environment)
diff --git a/vel/openai/baselines/common/vec_env/dummy_vec_env.py b/vel/openai/baselines/common/vec_env/dummy_vec_env.py
index 4f5c106f..ea21f130 100644
--- a/vel/openai/baselines/common/vec_env/dummy_vec_env.py
+++ b/vel/openai/baselines/common/vec_env/dummy_vec_env.py
@@ -1,8 +1,8 @@
 import numpy as np
-from gym import spaces
 from . import VecEnv
 from .util import copy_obs_dict, dict_to_obs, obs_space_info
 
+
 class DummyVecEnv(VecEnv):
     """
     VecEnv that does runs multiple environments sequentially, that is,
@@ -13,6 +13,7 @@ class DummyVecEnv(VecEnv):
     def __init__(self, env_fns):
         """
         Arguments:
+
         env_fns: iterable of callables      functions that build environments
         """
         self.envs = [fn() for fn in env_fns]
@@ -26,7 +27,7 @@ def __init__(self, env_fns):
         self.buf_rews  = np.zeros((self.num_envs,), dtype=np.float32)
         self.buf_infos = [{} for _ in range(self.num_envs)]
         self.actions = None
-        self.specs = [e.spec for e in self.envs]
+        self.spec = self.envs[0].spec
 
     def step_async(self, actions):
         listify = True
@@ -45,8 +46,8 @@ def step_async(self, actions):
     def step_wait(self):
         for e in range(self.num_envs):
             action = self.actions[e]
-            if isinstance(self.envs[e].action_space, spaces.Discrete):
-                action = int(action)
+            # if isinstance(self.envs[e].action_space, spaces.Discrete):
+            #    action = int(action)
 
             obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action)
             if self.buf_dones[e]:
diff --git a/vel/openai/baselines/common/vec_env/shmem_vec_env.py b/vel/openai/baselines/common/vec_env/shmem_vec_env.py
index 4d941043..fcee5ad5 100644
--- a/vel/openai/baselines/common/vec_env/shmem_vec_env.py
+++ b/vel/openai/baselines/common/vec_env/shmem_vec_env.py
@@ -2,12 +2,12 @@
 An interface for asynchronous vectorized environments.
 """
 
-from multiprocessing import Pipe, Array, Process
-
+import multiprocessing as mp
 import numpy as np
 from . import VecEnv, CloudpickleWrapper
 import ctypes
 from vel.openai.baselines import logger
+from . import clear_mpi_env_vars
 
 from .util import dict_to_obs, obs_space_info, obs_to_dict
 
@@ -23,11 +23,12 @@ class ShmemVecEnv(VecEnv):
     Optimized version of SubprocVecEnv that uses shared variables to communicate observations.
     """
 
-    def __init__(self, env_fns, spaces=None):
+    def __init__(self, env_fns, spaces=None, context='spawn'):
         """
         If you don't specify observation_space, we'll have to create a dummy
         environment to get it.
         """
+        ctx = mp.get_context(context)
         if spaces:
             observation_space, action_space = spaces
         else:
@@ -40,22 +41,24 @@ def __init__(self, env_fns, spaces=None):
         VecEnv.__init__(self, len(env_fns), observation_space, action_space)
         self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
         self.obs_bufs = [
-            {k: Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
+            {k: ctx.Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in
+             self.obs_keys}
             for _ in env_fns]
         self.parent_pipes = []
         self.procs = []
-        for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
-            wrapped_fn = CloudpickleWrapper(env_fn)
-            parent_pipe, child_pipe = Pipe()
-            proc = Process(target=_subproc_worker,
-                           args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
-            proc.daemon = True
-            self.procs.append(proc)
-            self.parent_pipes.append(parent_pipe)
-            proc.start()
-            child_pipe.close()
+        with clear_mpi_env_vars():
+            for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
+                wrapped_fn = CloudpickleWrapper(env_fn)
+                parent_pipe, child_pipe = ctx.Pipe()
+                proc = ctx.Process(target=_subproc_worker,
+                                   args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes,
+                                         self.obs_dtypes, self.obs_keys))
+                proc.daemon = True
+                self.procs.append(proc)
+                self.parent_pipes.append(parent_pipe)
+                proc.start()
+                child_pipe.close()
         self.waiting_step = False
-        self.specs = [f().spec for f in env_fns]
         self.viewer = None
 
     def reset(self):
@@ -70,9 +73,11 @@ def step_async(self, actions):
         assert len(actions) == len(self.parent_pipes)
         for pipe, act in zip(self.parent_pipes, actions):
             pipe.send(('step', act))
+        self.waiting_step = True
 
     def step_wait(self):
         outs = [pipe.recv() for pipe in self.parent_pipes]
+        self.waiting_step = False
         obs, rews, dones, infos = zip(*outs)
         return self._decode_obses(obs), np.array(rews), np.array(dones), infos
 
@@ -95,18 +100,17 @@ def get_images(self, mode='human'):
     def _decode_obses(self, obs):
         result = {}
         for k in self.obs_keys:
-
             bufs = [b[k] for b in self.obs_bufs]
             o = [np.frombuffer(b.get_obj(), dtype=self.obs_dtypes[k]).reshape(self.obs_shapes[k]) for b in bufs]
             result[k] = np.array(o)
         return dict_to_obs(result)
 
-
 def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys):
     """
     Control a single environment instance using IPC and
     shared memory.
     """
+
     def _write_obs(maybe_dict_obs):
         flatdict = obs_to_dict(maybe_dict_obs)
         for k in keys:
diff --git a/vel/openai/baselines/common/vec_env/subproc_vec_env.py b/vel/openai/baselines/common/vec_env/subproc_vec_env.py
index fe46ff06..a5b72ace 100644
--- a/vel/openai/baselines/common/vec_env/subproc_vec_env.py
+++ b/vel/openai/baselines/common/vec_env/subproc_vec_env.py
@@ -1,6 +1,7 @@
+import multiprocessing as mp
+
 import numpy as np
-from multiprocessing import Process, Pipe
-from . import VecEnv, CloudpickleWrapper
+from . import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
 
 
 def worker(remote, parent_remote, env_fn_wrapper):
@@ -22,8 +23,8 @@ def worker(remote, parent_remote, env_fn_wrapper):
             elif cmd == 'close':
                 remote.close()
                 break
-            elif cmd == 'get_spaces':
-                remote.send((env.observation_space, env.action_space))
+            elif cmd == 'get_spaces_spec':
+                remote.send((env.observation_space, env.action_space, env.spec))
             else:
                 raise NotImplementedError
     except KeyboardInterrupt:
@@ -37,7 +38,7 @@ class SubprocVecEnv(VecEnv):
     VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes.
     Recommended to use when num_envs > 1 and step() can be a bottleneck.
     """
-    def __init__(self, env_fns, spaces=None):
+    def __init__(self, env_fns, spaces=None, context='spawn'):
         """
         Arguments:
 
@@ -46,19 +47,20 @@ def __init__(self, env_fns, spaces=None):
         self.waiting = False
         self.closed = False
         nenvs = len(env_fns)
-        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
-        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
+        ctx = mp.get_context(context)
+        self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(nenvs)])
+        self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
                    for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
         for p in self.ps:
             p.daemon = True  # if the main process crashes, we should not cause things to hang
-            p.start()
+            with clear_mpi_env_vars():
+                p.start()
         for remote in self.work_remotes:
             remote.close()
 
-        self.remotes[0].send(('get_spaces', None))
-        observation_space, action_space = self.remotes[0].recv()
+        self.remotes[0].send(('get_spaces_spec', None))
+        observation_space, action_space, self.spec = self.remotes[0].recv()
         self.viewer = None
-        self.specs = [f().spec for f in env_fns]
         VecEnv.__init__(self, len(env_fns), observation_space, action_space)
 
     def step_async(self, actions):
@@ -100,16 +102,16 @@ def get_images(self):
     def _assert_not_closed(self):
         assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()"
 
+    def __del__(self):
+        if not self.closed:
+            self.close()
 
 def _flatten_obs(obs):
-    assert isinstance(obs, list) or isinstance(obs, tuple)
+    assert isinstance(obs, (list, tuple))
     assert len(obs) > 0
 
     if isinstance(obs[0], dict):
-        import collections
-        assert isinstance(obs, collections.OrderedDict)
         keys = obs[0].keys()
         return {k: np.stack([o[k] for o in obs]) for k in keys}
     else:
         return np.stack(obs)
-
diff --git a/vel/openai/baselines/common/wrappers.py b/vel/openai/baselines/common/wrappers.py
new file mode 100644
index 00000000..95919264
--- /dev/null
+++ b/vel/openai/baselines/common/wrappers.py
@@ -0,0 +1,31 @@
+import gym
+
+
+class TimeLimit(gym.Wrapper):
+    def __init__(self, env, max_episode_steps=None):
+        super(TimeLimit, self).__init__(env)
+        self._max_episode_steps = max_episode_steps
+        self._elapsed_steps = 0
+
+    def step(self, ac):
+        observation, reward, done, info = self.env.step(ac)
+        self._elapsed_steps += 1
+        if self._elapsed_steps >= self._max_episode_steps:
+            done = True
+            info['TimeLimit.truncated'] = True
+        return observation, reward, done, info
+
+    def reset(self, **kwargs):
+        self._elapsed_steps = 0
+        return self.env.reset(**kwargs)
+
+
+class ClipActionsWrapper(gym.Wrapper):
+    def step(self, action):
+        import numpy as np
+        action = np.nan_to_num(action)
+        action = np.clip(action, self.action_space.low, self.action_space.high)
+        return self.env.step(action)
+
+    def reset(self, **kwargs):
+        return self.env.reset(**kwargs)
diff --git a/vel/openai/baselines/logger.py b/vel/openai/baselines/logger.py
index 05eab9ac..e92776ca 100644
--- a/vel/openai/baselines/logger.py
+++ b/vel/openai/baselines/logger.py
@@ -7,6 +7,7 @@
 import datetime
 import tempfile
 from collections import defaultdict
+from contextlib import contextmanager
 
 DEBUG = 10
 INFO = 20
@@ -37,8 +38,8 @@ def writekvs(self, kvs):
         # Create strings for printing
         key2str = {}
         for (key, val) in sorted(kvs.items()):
-            if isinstance(val, float):
-                valstr = '%-8.3g' % (val,)
+            if hasattr(val, '__float__'):
+                valstr = '%-8.3g' % val
             else:
                 valstr = str(val)
             key2str[self._truncate(key)] = self._truncate(valstr)
@@ -68,7 +69,8 @@ def writekvs(self, kvs):
         self.file.flush()
 
     def _truncate(self, s):
-        return s[:20] + '...' if len(s) > 23 else s
+        maxlen = 30
+        return s[:maxlen-3] + '...' if len(s) > maxlen else s
 
     def writeseq(self, seq):
         seq = list(seq)
@@ -90,7 +92,6 @@ def __init__(self, filename):
     def writekvs(self, kvs):
         for k, v in sorted(kvs.items()):
             if hasattr(v, 'dtype'):
-                v = v.tolist()
                 kvs[k] = float(v)
         self.file.write(json.dumps(kvs) + '\n')
         self.file.flush()
@@ -195,13 +196,13 @@ def logkv(key, val):
     Call this once for each diagnostic quantity, each iteration
     If called many times, last value will be used.
     """
-    Logger.CURRENT.logkv(key, val)
+    get_current().logkv(key, val)
 
 def logkv_mean(key, val):
     """
     The same as logkv(), but if called many times, values averaged.
     """
-    Logger.CURRENT.logkv_mean(key, val)
+    get_current().logkv_mean(key, val)
 
 def logkvs(d):
     """
@@ -213,21 +214,18 @@ def logkvs(d):
 def dumpkvs():
     """
     Write all of the diagnostics from the current iteration
-
-    level: int. (see logger.py docs) If the global logger level is higher than
-                the level argument here, don't print to stdout.
     """
-    Logger.CURRENT.dumpkvs()
+    return get_current().dumpkvs()
 
 def getkvs():
-    return Logger.CURRENT.name2val
+    return get_current().name2val
 
 
 def log(*args, level=INFO):
     """
     Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
     """
-    Logger.CURRENT.log(*args, level=level)
+    get_current().log(*args, level=level)
 
 def debug(*args):
     log(*args, level=DEBUG)
@@ -246,30 +244,29 @@ def set_level(level):
     """
     Set logging threshold on current logger.
     """
-    Logger.CURRENT.set_level(level)
+    get_current().set_level(level)
+
+def set_comm(comm):
+    get_current().set_comm(comm)
 
 def get_dir():
     """
     Get directory that log files are being written to.
     will be None if there is no output directory (i.e., if you didn't call start)
     """
-    return Logger.CURRENT.get_dir()
+    return get_current().get_dir()
 
 record_tabular = logkv
 dump_tabular = dumpkvs
 
-class ProfileKV:
-    """
-    Usage:
-    with logger.ProfileKV("interesting_scope"):
-        code
-    """
-    def __init__(self, n):
-        self.n = "wait_" + n
-    def __enter__(self):
-        self.t1 = time.time()
-    def __exit__(self ,type, value, traceback):
-        Logger.CURRENT.name2val[self.n] += time.time() - self.t1
+@contextmanager
+def profile_kv(scopename):
+    logkey = 'wait_' + scopename
+    tstart = time.time()
+    try:
+        yield
+    finally:
+        get_current().name2val[logkey] += time.time() - tstart
 
 def profile(n):
     """
@@ -279,7 +276,7 @@ def my_func(): code
     """
     def decorator_with_name(func):
         def func_wrapper(*args, **kwargs):
-            with ProfileKV(n):
+            with profile_kv(n):
                 return func(*args, **kwargs)
         return func_wrapper
     return decorator_with_name
@@ -289,17 +286,25 @@ def func_wrapper(*args, **kwargs):
 # Backend
 # ================================================================
 
+def get_current():
+    if Logger.CURRENT is None:
+        _configure_default_logger()
+
+    return Logger.CURRENT
+
+
 class Logger(object):
     DEFAULT = None  # A logger with no output files. (See right below class definition)
                     # So that you can still log to the terminal without setting up any output files
     CURRENT = None  # Current logger being used by the free functions above
 
-    def __init__(self, dir, output_formats):
+    def __init__(self, dir, output_formats, comm=None):
         self.name2val = defaultdict(float)  # values this iteration
         self.name2cnt = defaultdict(int)
         self.level = INFO
         self.dir = dir
         self.output_formats = output_formats
+        self.comm = comm
 
     # Logging API, forwarded
     # ----------------------------------------
@@ -307,20 +312,19 @@ def logkv(self, key, val):
         self.name2val[key] = val
 
     def logkv_mean(self, key, val):
-        if val is None:
-            self.name2val[key] = None
-            return
         oldval, cnt = self.name2val[key], self.name2cnt[key]
         self.name2val[key] = oldval*cnt/(cnt+1) + val/(cnt+1)
         self.name2cnt[key] = cnt + 1
 
     def dumpkvs(self):
-        if self.level == DISABLED: return
+        d = self.name2val
+        out = d.copy()  # Return the dict for unit testing purposes
         for fmt in self.output_formats:
             if isinstance(fmt, KVWriter):
-                fmt.writekvs(self.name2val)
+                fmt.writekvs(d)
         self.name2val.clear()
         self.name2cnt.clear()
+        return out
 
     def log(self, *args, level=INFO):
         if self.level <= level:
@@ -331,6 +335,9 @@ def log(self, *args, level=INFO):
     def set_level(self, level):
         self.level = level
 
+    def set_comm(self, comm):
+        self.comm = comm
+
     def get_dir(self):
         return self.dir
 
@@ -345,7 +352,19 @@ def _do_log(self, args):
             if isinstance(fmt, SeqWriter):
                 fmt.writeseq(map(str, args))
 
-def configure(dir=None, format_strs=None):
+def get_rank_without_mpi_import():
+    # check environment variables here instead of importing mpi4py
+    # to avoid calling MPI_Init() when this module is imported
+    for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']:
+        if varname in os.environ:
+            return int(os.environ[varname])
+    return 0
+
+
+def configure(dir=None, format_strs=None, comm=None, log_suffix=''):
+    """
+    If comm is provided, average all numerical stats across that comm
+    """
     if dir is None:
         dir = os.getenv('OPENAI_LOGDIR')
     if dir is None:
@@ -354,15 +373,9 @@ def configure(dir=None, format_strs=None):
     assert isinstance(dir, str)
     os.makedirs(dir, exist_ok=True)
 
-    log_suffix = ''
-    rank = 0
-    # check environment variables here instead of importing mpi4py
-    # to avoid calling MPI_Init() when this module is imported
-    for varname in ['PMI_RANK', 'OMPI_COMM_WORLD_RANK']:
-        if varname in os.environ:
-            rank = int(os.environ[varname])
+    rank = get_rank_without_mpi_import()
     if rank > 0:
-        log_suffix = "-rank%03i" % rank
+        log_suffix = log_suffix + "-rank%03i" % rank
 
     if format_strs is None:
         if rank == 0:
@@ -372,15 +385,11 @@ def configure(dir=None, format_strs=None):
     format_strs = filter(None, format_strs)
     output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]
 
-    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats)
-    # log('Logging to %s'%dir)
+    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
+    log('Logging to %s'%dir)
 
 def _configure_default_logger():
-    format_strs = None
-    # keep the old default of only writing to stdout
-    if 'OPENAI_LOG_FORMAT' not in os.environ:
-        format_strs = ['stdout']
-    configure(format_strs=format_strs)
+    configure()
     Logger.DEFAULT = Logger.CURRENT
 
 def reset():
@@ -389,17 +398,15 @@ def reset():
         Logger.CURRENT = Logger.DEFAULT
         log('Reset logger')
 
-class scoped_configure(object):
-    def __init__(self, dir=None, format_strs=None):
-        self.dir = dir
-        self.format_strs = format_strs
-        self.prevlogger = None
-    def __enter__(self):
-        self.prevlogger = Logger.CURRENT
-        configure(dir=self.dir, format_strs=self.format_strs)
-    def __exit__(self, *args):
+@contextmanager
+def scoped_configure(dir=None, format_strs=None, comm=None):
+    prevlogger = Logger.CURRENT
+    configure(dir=dir, format_strs=format_strs, comm=comm)
+    try:
+        yield
+    finally:
         Logger.CURRENT.close()
-        Logger.CURRENT = self.prevlogger
+        Logger.CURRENT = prevlogger
 
 # ================================================================
 
@@ -423,7 +430,7 @@ def _demo():
     logkv_mean("b", -44.4)
     logkv("a", 5.5)
     dumpkvs()
-    info("^^^ should see b = 33.3")
+    info("^^^ should see b = -33.3")
 
     logkv("b", -2.5)
     dumpkvs()
@@ -456,7 +463,6 @@ def read_tb(path):
     import pandas
     import numpy as np
     from glob import glob
-    from collections import defaultdict
     import tensorflow as tf
     if osp.isdir(path):
         fnames = glob(osp.join(path, "events.*"))
@@ -482,8 +488,5 @@ def read_tb(path):
             data[step-1, colidx] = value
     return pandas.DataFrame(data, columns=tags)
 
-# configure the default logger on import
-_configure_default_logger()
-
 if __name__ == "__main__":
     _demo()
diff --git a/vel/rl/reinforcers/__init__.py b/vel/optimizer/__init__.py
similarity index 100%
rename from vel/rl/reinforcers/__init__.py
rename to vel/optimizer/__init__.py
diff --git a/vel/optimizer/adadelta.py b/vel/optimizer/adadelta.py
new file mode 100644
index 00000000..108cd72e
--- /dev/null
+++ b/vel/optimizer/adadelta.py
@@ -0,0 +1,38 @@
+import typing
+
+from torch.optim.adadelta import Adadelta
+
+from vel.api import OptimizerFactory, VelOptimizerProxy, VelOptimizer
+
+
+class AdadeltaFactory(OptimizerFactory):
+    """ Adadelta optimizer factory """
+
+    def __init__(self, lr: float = 1.0, rho: float = 0.9, eps: float = 1e-6, weight_decay: float = 0.0,
+                 max_grad_norm: typing.Optional[float] = None):
+        super().__init__()
+        self.lr = lr
+        self.rho = rho
+        self.eps = eps
+        self.weight_decay = weight_decay
+        self.max_grad_norm = max_grad_norm
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        optimizer_params, group_names = self.preprocess(parameters)
+
+        return VelOptimizerProxy(Adadelta(
+            optimizer_params,
+            lr=self.lr, rho=self.rho, eps=self.eps, weight_decay=self.weight_decay
+        ), group_names, self.max_grad_norm)
+
+
+def create(lr: float = 1.0, rho: float = 0.9, eps: float = 1e-6, weight_decay: float = 0.0,
+           max_grad_norm: typing.Optional[float] = None, parameter_groups=None):
+    """ Vel factory function """
+    return AdadeltaFactory(
+        lr=lr,
+        rho=rho,
+        eps=eps,
+        weight_decay=weight_decay,
+        max_grad_norm=max_grad_norm
+    ).with_parameter_groups(parameter_groups)
diff --git a/vel/optimizer/adam.py b/vel/optimizer/adam.py
new file mode 100644
index 00000000..8e9fb575
--- /dev/null
+++ b/vel/optimizer/adam.py
@@ -0,0 +1,34 @@
+import typing
+
+from torch.optim.adam import Adam
+
+from vel.api import OptimizerFactory, VelOptimizer, VelOptimizerProxy
+
+
+class AdamFactory(OptimizerFactory):
+    """ Adam optimizer factory """
+
+    def __init__(self, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False,
+                 max_grad_norm: typing.Optional[float] = None):
+        super().__init__()
+        self.lr = lr
+        self.betas = betas
+        self.eps = eps
+        self.weight_decay = weight_decay
+        self.amsgrad = amsgrad
+        self.max_grad_norm = max_grad_norm
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        optimizer_params, group_names = self.preprocess(parameters)
+
+        return VelOptimizerProxy(Adam(
+            optimizer_params,
+            lr=self.lr, betas=self.betas, eps=self.eps, weight_decay=self.weight_decay, amsgrad=self.amsgrad
+        ), group_names, self.max_grad_norm)
+
+
+def create(lr, betas=(0.9, 0.999), weight_decay=0, epsilon=1e-8, max_grad_norm=None, parameter_groups=None):
+    """ Vel factory function """
+    return AdamFactory(
+        lr=lr, betas=betas, weight_decay=weight_decay, eps=epsilon, max_grad_norm=max_grad_norm,
+    ).with_parameter_groups(parameter_groups)
diff --git a/vel/optimizer/radam.py b/vel/optimizer/radam.py
new file mode 100644
index 00000000..b6c6e825
--- /dev/null
+++ b/vel/optimizer/radam.py
@@ -0,0 +1,113 @@
+"""
+RAdam implementation from: https://github.com/LiyuanLucasLiu/RAdam/blob/master/cifar_imagenet/utils/radam.py
+"""
+import math
+import torch
+import typing
+
+from torch.optim.optimizer import Optimizer
+
+from vel.api import OptimizerFactory, VelOptimizer, VelOptimizerProxy
+
+
+class RAdam(Optimizer):
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+        self.buffer = [[None, None, None] for ind in range(10)]
+        super(RAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(RAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError('RAdam does not support sparse gradients')
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                buffered = self.buffer[int(state['step'] % 10)]
+                if state['step'] == buffered[0]:
+                    N_sma, step_size = buffered[1], buffered[2]
+                else:
+                    buffered[0] = state['step']
+                    beta2_t = beta2 ** state['step']
+                    N_sma_max = 2 / (1 - beta2) - 1
+                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+                    buffered[1] = N_sma
+
+                    # more conservative since it's an approximated value
+                    if N_sma >= 5:
+                        step_size = group['lr'] * math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
+                    else:
+                        step_size = group['lr'] / (1 - beta1 ** state['step'])
+                    buffered[2] = step_size
+
+                if group['weight_decay'] != 0:
+                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+                else:
+                    p_data_fp32.add_(-step_size, exp_avg)
+
+                p.data.copy_(p_data_fp32)
+
+        return loss
+
+
+class RAdamFactory(OptimizerFactory):
+    """ Adam optimizer factory """
+
+    def __init__(self, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0,
+                 max_grad_norm: typing.Optional[float] = None):
+        super().__init__()
+        self.lr = lr
+        self.betas = betas
+        self.eps = eps
+        self.weight_decay = weight_decay
+        self.max_grad_norm = max_grad_norm
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        optimizer_params, group_names = self.preprocess(parameters)
+
+        return VelOptimizerProxy(RAdam(
+            optimizer_params,
+            lr=self.lr, betas=self.betas, eps=self.eps, weight_decay=self.weight_decay
+        ), group_names, self.max_grad_norm)
+
+
+def create(lr, betas=(0.9, 0.999), weight_decay=0, epsilon=1e-8, max_grad_norm=None, parameter_groups=None):
+    """ Vel factory function """
+    return RAdamFactory(
+        lr=lr, betas=betas, weight_decay=weight_decay, eps=epsilon, max_grad_norm=max_grad_norm
+    ).with_parameter_groups(parameter_groups)
diff --git a/vel/optimizer/ranger.py b/vel/optimizer/ranger.py
new file mode 100644
index 00000000..1c3143b6
--- /dev/null
+++ b/vel/optimizer/ranger.py
@@ -0,0 +1,180 @@
+# Ranger deep learning optimizer - RAdam + Lookahead combined.
+# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
+
+import math
+import torch
+import typing
+
+from torch.optim.optimizer import Optimizer
+
+from vel.api import OptimizerFactory, VelOptimizer, VelOptimizerProxy
+
+
+class Ranger(Optimizer):
+
+    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95,0.999), eps=1e-5, weight_decay=0):
+        #parameter checks
+        if not 0.0 <= alpha <= 1.0:
+            raise ValueError(f'Invalid slow update rate: {alpha}')
+        if not 1 <= k:
+            raise ValueError(f'Invalid lookahead steps: {k}')
+        if not lr > 0:
+            raise ValueError(f'Invalid Learning Rate: {lr}')
+        if not eps > 0:
+            raise ValueError(f'Invalid eps: {eps}')
+
+        #parameter comments:
+        # beta1 (momentum) of .95 seems to work better than .90...
+        #N_sma_threshold of 5 seems better in testing than 4.
+        #In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.
+
+        #prep defaults and init torch.optim base
+        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)
+        super().__init__(params,defaults)
+
+        #adjustable threshold
+        self.N_sma_threshhold = N_sma_threshhold
+
+        #now we can get to work...
+        #removed as we now use step from RAdam...no need for duplicate step counting
+        #for group in self.param_groups:
+        #    group["step_counter"] = 0
+            #print("group step counter init")
+
+        #look ahead params
+        self.alpha = alpha
+        self.k = k
+
+        #radam buffer for state
+        self.radam_buffer = [[None,None,None] for ind in range(10)]
+
+        #self.first_run_check=0
+
+        #lookahead weights
+        #9/2/19 - lookahead param tensors have been moved to state storage.
+        #This should resolve issues with load/save where weights were left in GPU memory from first load, slowing down future runs.
+
+        #self.slow_weights = [[p.clone().detach() for p in group['params']]
+        #                     for group in self.param_groups]
+
+        #don't use grad for lookahead weights
+        #for w in it.chain(*self.slow_weights):
+        #    w.requires_grad = False
+
+    def __setstate__(self, state):
+        print("set state called")
+        super(Ranger, self).__setstate__(state)
+
+
+    def step(self, closure=None):
+        loss = None
+        #note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.
+        #Uncomment if you need to use the actual closure...
+
+        #if closure is not None:
+            #loss = closure()
+
+        #Evaluate averages and grad, update param tensors
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError('Ranger optimizer does not support sparse gradients')
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]  #get state dict for this param
+
+                if len(state) == 0:   #if first time to run...init dictionary with our desired entries
+                    #if self.first_run_check==0:
+                        #self.first_run_check=1
+                        #print("Initializing slow buffer...should not see this at load from saved model!")
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+
+                    #look ahead weight storage now in state dict
+                    state['slow_buffer'] = torch.empty_like(p.data)
+                    state['slow_buffer'].copy_(p.data)
+
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
+
+                #begin computations
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                #compute variance mov avg
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                #compute mean moving avg
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+
+
+                buffered = self.radam_buffer[int(state['step'] % 10)]
+                if state['step'] == buffered[0]:
+                    N_sma, step_size = buffered[1], buffered[2]
+                else:
+                    buffered[0] = state['step']
+                    beta2_t = beta2 ** state['step']
+                    N_sma_max = 2 / (1 - beta2) - 1
+                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+                    buffered[1] = N_sma
+                    if N_sma > self.N_sma_threshhold:
+                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
+                    else:
+                        step_size = 1.0 / (1 - beta1 ** state['step'])
+                    buffered[2] = step_size
+
+                if group['weight_decay'] != 0:
+                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+
+                if N_sma > self.N_sma_threshhold:
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
+                else:
+                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
+
+                p.data.copy_(p_data_fp32)
+
+                #integrated look ahead...
+                #we do it at the param level instead of group level
+                if state['step'] % group['k'] == 0:
+                    slow_p = state['slow_buffer'] #get access to slow param tensor
+                    slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha
+                    p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor
+
+        return loss
+
+
+class RangerFactory(OptimizerFactory):
+    """ Adam optimizer factory """
+
+    def __init__(self, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0,
+                 max_grad_norm: typing.Optional[float] = None):
+        super().__init__()
+        self.lr = lr
+        self.betas = betas
+        self.eps = eps
+        self.weight_decay = weight_decay
+        self.max_grad_norm = max_grad_norm
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        optimizer_params, group_names = self.preprocess(parameters)
+
+        return VelOptimizerProxy(Ranger(
+            optimizer_params,
+            lr=self.lr, betas=self.betas, eps=self.eps, weight_decay=self.weight_decay
+        ), group_names, self.max_grad_norm)
+
+
+def create(lr, betas=(0.9, 0.999), weight_decay=0, epsilon=1e-8, max_grad_norm=None, parameter_groups=None):
+    """ Vel factory function """
+    return RangerFactory(
+        lr=lr, betas=betas, weight_decay=weight_decay, eps=epsilon, max_grad_norm=max_grad_norm
+    ).with_parameter_groups(parameter_groups)
diff --git a/vel/optimizer/rmsprop.py b/vel/optimizer/rmsprop.py
new file mode 100644
index 00000000..c967431a
--- /dev/null
+++ b/vel/optimizer/rmsprop.py
@@ -0,0 +1,37 @@
+import typing
+
+from torch.optim.rmsprop import RMSprop
+
+from vel.api import OptimizerFactory, VelOptimizerProxy, VelOptimizer
+
+
+class RMSpropFactory(OptimizerFactory):
+    """ RMSprop optimizer factory """
+
+    def __init__(self, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False,
+                 max_grad_norm: typing.Optional[float] = None):
+        super().__init__()
+        self.lr = lr
+        self.alpha = alpha
+        self.eps = eps
+        self.weight_decay = weight_decay
+        self.momentum = momentum
+        self.centered = centered
+        self.max_grad_norm = max_grad_norm
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        optimizer_params, group_names = self.preprocess(parameters)
+
+        return VelOptimizerProxy(RMSprop(
+            optimizer_params,
+            lr=self.lr, alpha=self.alpha, eps=self.eps,
+            weight_decay=self.weight_decay, momentum=self.momentum, centered=self.centered
+        ), group_names, self.max_grad_norm)
+
+
+def create(lr, alpha, momentum=0, weight_decay=0, epsilon=1e-8, max_grad_norm=None, parameter_groups=None):
+    """ Vel factory function """
+    return RMSpropFactory(
+        lr=lr, alpha=alpha, momentum=momentum, weight_decay=weight_decay, eps=float(epsilon),
+        max_grad_norm=max_grad_norm
+    ).with_parameter_groups(parameter_groups)
diff --git a/vel/optimizers/rmsprop_tf.py b/vel/optimizer/rmsprop_tf.py
similarity index 84%
rename from vel/optimizers/rmsprop_tf.py
rename to vel/optimizer/rmsprop_tf.py
index 5b0197da..7fea5b97 100644
--- a/vel/optimizers/rmsprop_tf.py
+++ b/vel/optimizer/rmsprop_tf.py
@@ -1,9 +1,12 @@
-import torch.optim
+import torch
+import typing
 
-from vel.api import OptimizerFactory, Model
+from torch.optim.optimizer import Optimizer
 
+from vel.api import OptimizerFactory, VelOptimizer, VelOptimizerProxy
 
-class RMSpropTF(torch.optim.Optimizer):
+
+class RMSpropTF(Optimizer):
     """Implements RMSprop algorithm. A TensorFlow version with epsilon under the square root
 
     Proposed by G. Hinton in his
@@ -113,22 +116,30 @@ def step(self, closure=None):
 class RMSpropTFFactory(OptimizerFactory):
     """ RMSprop optimizer factory - A Tensorflow version with epsilon under square root """
 
-    def __init__(self, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False):
+    def __init__(self, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False,
+                 max_grad_norm: typing.Optional[float] = None):
+        super().__init__()
         self.lr = lr
         self.alpha = alpha
         self.eps = eps
         self.weight_decay = weight_decay
         self.momentum = momentum
         self.centered = centered
+        self.max_grad_norm = max_grad_norm
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        optimizer_params, group_names = self.preprocess(parameters)
 
-    def instantiate(self, model: Model) -> RMSpropTF:
-        return RMSpropTF(
-            filter(lambda p: p.requires_grad, model.parameters()),
+        return VelOptimizerProxy(RMSpropTF(
+            optimizer_params,
             lr=self.lr, alpha=self.alpha, eps=self.eps,
             weight_decay=self.weight_decay, momentum=self.momentum, centered=self.centered
-        )
+        ), group_names, self.max_grad_norm)
 
 
-def create(lr, alpha, momentum=0, weight_decay=0, epsilon=1e-8):
+def create(lr, alpha, momentum=0, weight_decay=0, epsilon=1e-8, max_grad_norm=None, parameter_groups=None):
     """ Vel factory function """
-    return RMSpropTFFactory(lr=lr, alpha=alpha, momentum=momentum, weight_decay=weight_decay, eps=float(epsilon))
+    return RMSpropTFFactory(
+        lr=lr, alpha=alpha, momentum=momentum, weight_decay=weight_decay, eps=float(epsilon),
+        max_grad_norm=max_grad_norm
+    ).with_parameter_groups(parameter_groups)
diff --git a/vel/optimizer/sgd.py b/vel/optimizer/sgd.py
new file mode 100644
index 00000000..4479b41f
--- /dev/null
+++ b/vel/optimizer/sgd.py
@@ -0,0 +1,37 @@
+import typing
+
+from torch.optim.sgd import SGD
+
+from vel.api import OptimizerFactory, VelOptimizer, VelOptimizerProxy
+
+
+class SgdFactory(OptimizerFactory):
+    """ SGD optimizer factory """
+
+    def __init__(self, lr, momentum=0, dampening=0, weight_decay=0, nesterov=False,
+                 max_grad_norm: typing.Optional[float] = None):
+        super().__init__()
+        self.lr = lr
+        self.momentum = momentum
+        self.dampening = dampening
+        self.weight_decay = weight_decay
+        self.nesterov = nesterov
+        self.max_grad_norm = max_grad_norm
+
+    def instantiate(self, parameters) -> VelOptimizer:
+        optimizer_params, group_names = self.preprocess(parameters)
+
+        return VelOptimizerProxy(SGD(
+            optimizer_params,
+            lr=self.lr, momentum=self.momentum, dampening=self.dampening, weight_decay=self.weight_decay,
+            nesterov=self.nesterov
+        ), group_names, self.max_grad_norm)
+
+
+def create(lr, momentum=0, dampening=0, weight_decay=0, nesterov=False,
+           max_grad_norm: typing.Optional[float] = None, parameter_groups=None):
+    """ Vel factory function """
+    return SgdFactory(
+        lr=lr, momentum=momentum, dampening=dampening,
+        weight_decay=weight_decay, nesterov=nesterov, max_grad_norm=max_grad_norm
+    ).with_parameter_groups(parameter_groups)
diff --git a/vel/optimizers/adadelta.py b/vel/optimizers/adadelta.py
deleted file mode 100644
index 7bdc5529..00000000
--- a/vel/optimizers/adadelta.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import torch.optim
-
-from vel.api import OptimizerFactory, Model
-
-
-class AdadeltaFactory(OptimizerFactory):
-    """ Adadelta optimizer factory """
-
-    def __init__(self, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0):
-        self.lr = lr
-        self.rho = rho
-        self.eps = eps
-        self.weight_decay = weight_decay
-
-    def instantiate(self, model: Model) -> torch.optim.Adadelta:
-        return torch.optim.Adadelta(
-            filter(lambda p: p.requires_grad, model.parameters()),
-            lr=self.lr, rho=self.rho, eps=self.eps, weight_decay=self.weight_decay
-        )
-
-
-def create():
-    """ Vel factory function """
-    return AdadeltaFactory()
diff --git a/vel/optimizers/adam.py b/vel/optimizers/adam.py
deleted file mode 100644
index 5bda0c4f..00000000
--- a/vel/optimizers/adam.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import collections
-import torch.optim
-
-import vel.util.module_util as mu
-
-from vel.api import OptimizerFactory, Model
-
-
-class AdamFactory(OptimizerFactory):
-    """ Adam optimizer factory """
-
-    def __init__(self, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False, layer_groups=False):
-        self.lr = lr
-        self.betas = betas
-        self.eps = eps
-        self.weight_decay = weight_decay
-        self.amsgrad = amsgrad
-        self.layer_groups = layer_groups
-
-    def instantiate(self, model: Model) -> torch.optim.Adam:
-        if self.layer_groups:
-            parameters = mu.to_parameter_groups(model.get_layer_groups())
-
-            if isinstance(self.lr, collections.Sequence):
-                for idx, lr in enumerate(self.lr):
-                    parameters[idx]['lr'] = lr
-
-                default_lr = self.lr[0]
-            else:
-                default_lr = float(self.lr)
-
-            if isinstance(self.weight_decay, collections.Sequence):
-                for idx, weight_decay in enumerate(self.weight_decay):
-                    parameters[idx]['weight_decay'] = weight_decay
-
-                default_weight_decay = self.weight_decay[0]
-            else:
-                default_weight_decay = self.weight_decay
-
-            return torch.optim.Adam(
-                parameters,
-                lr=default_lr, betas=self.betas, eps=self.eps, weight_decay=default_weight_decay, amsgrad=self.amsgrad
-            )
-        else:
-            parameters = filter(lambda p: p.requires_grad, model.parameters())
-
-            return torch.optim.Adam(
-                parameters,
-                lr=self.lr, betas=self.betas, eps=self.eps, weight_decay=self.weight_decay, amsgrad=self.amsgrad
-            )
-
-
-def create(lr, betas=(0.9, 0.999), weight_decay=0, epsilon=1e-8, layer_groups=False):
-    """ Vel factory function """
-    return AdamFactory(lr=lr, betas=betas, weight_decay=weight_decay, eps=epsilon, layer_groups=layer_groups)
diff --git a/vel/optimizers/rmsprop.py b/vel/optimizers/rmsprop.py
deleted file mode 100644
index fd80f327..00000000
--- a/vel/optimizers/rmsprop.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import torch.optim
-
-from vel.api import OptimizerFactory, Model
-
-
-class RMSpropFactory(OptimizerFactory):
-    """ RMSprop optimizer factory """
-
-    def __init__(self, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False):
-        self.lr = lr
-        self.alpha = alpha
-        self.eps = eps
-        self.weight_decay = weight_decay
-        self.momentum = momentum
-        self.centered = centered
-
-    def instantiate(self, model: Model) -> torch.optim.RMSprop:
-        return torch.optim.RMSprop(
-            filter(lambda p: p.requires_grad, model.parameters()),
-            lr=self.lr, alpha=self.alpha, eps=self.eps,
-            weight_decay=self.weight_decay, momentum=self.momentum, centered=self.centered
-        )
-
-
-def create(lr, alpha, momentum=0, weight_decay=0, epsilon=1e-8):
-    """ Vel factory function """
-    return RMSpropFactory(lr=lr, alpha=alpha, momentum=momentum, weight_decay=weight_decay, eps=float(epsilon))
diff --git a/vel/optimizers/sgd.py b/vel/optimizers/sgd.py
deleted file mode 100644
index 93272f0b..00000000
--- a/vel/optimizers/sgd.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import torch.optim
-
-import vel.util.module_util as mu
-
-from vel.api import OptimizerFactory, Model
-
-
-class SgdFactory(OptimizerFactory):
-    """ SGD optimizer factory """
-
-    def __init__(self, lr, momentum=0, dampening=0, weight_decay=0, nesterov=False, layer_groups: bool=False):
-        self.lr = lr
-        self.momentum = momentum
-        self.dampening = dampening
-        self.weight_decay = weight_decay
-        self.nesterov = nesterov
-        self.layer_groups = layer_groups
-
-    def instantiate(self, model: Model) -> torch.optim.SGD:
-        if self.layer_groups:
-            parameters = mu.to_parameter_groups(model.get_layer_groups())
-        else:
-            parameters = filter(lambda p: p.requires_grad, model.parameters())
-
-        return torch.optim.SGD(
-            parameters,
-            lr=self.lr, momentum=self.momentum, dampening=self.dampening, weight_decay=self.weight_decay,
-            nesterov=self.nesterov
-        )
-
-
-def create(lr, weight_decay=0, momentum=0, layer_groups=False):
-    """ Vel factory function """
-    return SgdFactory(lr=lr, weight_decay=weight_decay, momentum=momentum, layer_groups=layer_groups)
diff --git a/vel/phase/freeze.py b/vel/phase/freeze.py
deleted file mode 100644
index 576e372a..00000000
--- a/vel/phase/freeze.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import vel.api as api
-
-
-class FreezePhase(api.EmptyTrainPhase):
-    """ Freeze the model """
-
-    def set_up_phase(self, training_info, model, source):
-        """ Freeze the model """
-        model.freeze()
-
-
-def create():
-    """ Vel factory function """
-    return FreezePhase()
diff --git a/vel/rl/algo/distributional_dqn.py b/vel/rl/algo/distributional_dqn.py
deleted file mode 100644
index dfa050ec..00000000
--- a/vel/rl/algo/distributional_dqn.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import torch
-import torch.nn.utils
-
-from vel.api import ModelFactory
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
-from vel.rl.api import OptimizerAlgoBase
-
-
-class DistributionalDeepQLearning(OptimizerAlgoBase):
-    """ Deep Q-Learning algorithm """
-
-    def __init__(self, model_factory: ModelFactory, discount_factor: float, double_dqn: bool,
-                 target_update_frequency: int, max_grad_norm: float):
-        super().__init__(max_grad_norm)
-
-        self.model_factory = model_factory
-        self.discount_factor = discount_factor
-
-        self.double_dqn = double_dqn
-        self.target_update_frequency = target_update_frequency
-
-        self.target_model = None
-
-        self.vmin = None
-        self.vmax = None
-        self.num_atoms = None
-        self.support_atoms = None
-        self.atom_delta = None
-
-    def initialize(self, training_info, model, environment, device):
-        """ Initialize policy gradient from reinforcer settings """
-        self.target_model = self.model_factory.instantiate(action_space=environment.action_space).to(device)
-        self.target_model.load_state_dict(model.state_dict())
-        self.target_model.eval()
-
-        histogram_info = model.histogram_info()
-
-        self.vmin = histogram_info['vmin']
-        self.vmax = histogram_info['vmax']
-
-        self.num_atoms = histogram_info['num_atoms']
-
-        self.support_atoms = histogram_info['support_atoms']
-        self.atom_delta = histogram_info['atom_delta']
-
-    def calculate_gradient(self, batch_info, device, model, rollout):
-        """ Calculate loss of the supplied rollout """
-        evaluator = model.evaluate(rollout)
-        batch_size = rollout.frames()
-
-        dones_tensor = evaluator.get('rollout:dones')
-        rewards_tensor = evaluator.get('rollout:rewards')
-
-        assert dones_tensor.dtype == torch.float32
-
-        with torch.no_grad():
-            target_evaluator = self.target_model.evaluate(rollout)
-
-            if self.double_dqn:
-                # DOUBLE DQN
-                # Histogram gets returned as logits initially, we need to exp it before projection
-                target_value_histogram_for_all_actions = target_evaluator.get('model:q_dist_next').exp()
-                model_value_histogram_for_all_actions = evaluator.get('model:q_dist_next').exp()
-
-                atoms_aligned = self.support_atoms.view(1, 1, self.num_atoms)
-
-                selected_action_indices = (
-                    (atoms_aligned * model_value_histogram_for_all_actions).sum(dim=-1).argmax(dim=1)
-                )
-
-                # Select largest 'target' value based on action that 'model' selects
-                next_value_histograms = (
-                    target_value_histogram_for_all_actions[range(batch_size), selected_action_indices]
-                )
-            else:
-                # REGULAR DQN
-                # Histogram gets returned as logits initially, we need to exp it before projection
-                target_value_histogram_for_all_actions = target_evaluator.get('model:q_dist_next').exp()
-
-                atoms_aligned = self.support_atoms.view(1, 1, self.num_atoms)
-
-                selected_action_indices = (
-                    (atoms_aligned * target_value_histogram_for_all_actions).sum(dim=-1).argmax(dim=1)
-                )
-
-                next_value_histograms = (
-                    target_value_histogram_for_all_actions[range(batch_size), selected_action_indices]
-                )
-
-            # HISTOGRAM PROJECTION CODE
-            forward_steps = rollout.extra_data.get('forward_steps', 1)
-
-            atoms_projected = (
-                rewards_tensor.unsqueeze(1) +
-                (self.discount_factor ** forward_steps) *
-                (1 - dones_tensor).unsqueeze(1) * self.support_atoms.unsqueeze(0)
-            )
-
-            atoms_projected = atoms_projected.clamp(min=self.vmin, max=self.vmax)
-            projection_indices = (atoms_projected - self.vmin) / self.atom_delta
-
-            index_floor = projection_indices.floor().long()
-            index_ceil = projection_indices.ceil().long()
-
-            # Fix corner case when index_floor == index_ceil
-            index_floor[(index_ceil > 0) * (index_floor == index_ceil)] -= 1
-            index_ceil[(index_floor < (self.num_atoms - 1)) * (index_floor == index_ceil)] += 1
-
-            value_histogram_projected = torch.zeros_like(next_value_histograms)
-
-            # Following part will be a bit convoluted, in an effort to fully vectorize projection operation
-
-            # Special offset index tensor
-            offsets = (
-                torch.arange(0, batch_size * self.num_atoms, self.num_atoms)
-                .unsqueeze(1)
-                .expand(batch_size, self.num_atoms)
-                .contiguous().view(-1).to(device)
-            )
-
-            # Linearize all the buffers
-            value_histogram_projected = value_histogram_projected.view(-1)
-            index_ceil = index_ceil.view(-1)
-            index_floor = index_floor.view(-1)
-            projection_indices = projection_indices.view(-1)
-
-            value_histogram_projected.index_add_(
-                0,
-                index_floor+offsets,
-                (next_value_histograms.view(-1) * (index_ceil.float() - projection_indices))
-            )
-
-            value_histogram_projected.index_add_(
-                0,
-                index_ceil+offsets,
-                (next_value_histograms.view(-1) * (projection_indices - index_floor.float()))
-            )
-
-            value_histogram_projected = value_histogram_projected.reshape(next_value_histograms.shape)
-
-        q_log_histogram_selected = evaluator.get('model:action:q_dist')
-
-        # Cross-entropy loss as usual
-        original_losses = -(value_histogram_projected * q_log_histogram_selected).sum(dim=1)
-
-        if evaluator.is_provided('rollout:weights'):
-            weights = evaluator.get('rollout:weights')
-        else:
-            weights = torch.ones_like(rewards_tensor)
-
-        loss_value = torch.mean(weights * original_losses)
-        loss_value.backward()
-
-        with torch.no_grad():
-            mean_q_model = (self.support_atoms.unsqueeze(0) * torch.exp(q_log_histogram_selected)).sum(dim=1).mean()
-            mean_q_target = (self.support_atoms.unsqueeze(0) * value_histogram_projected).sum(dim=1).mean()
-
-        return {
-            'loss': loss_value.item(),
-            # We need it to update priorities in the replay buffer:
-            'errors': original_losses.detach().cpu().numpy(),
-            'average_q_selected': mean_q_model.item(),
-            'average_q_target': mean_q_target.item()
-        }
-
-    def post_optimization_step(self, batch_info, device, model, rollout):
-        """ Steps to take after optimization has been done"""
-        if batch_info.aggregate_batch_number % self.target_update_frequency == 0:
-            self.target_model.load_state_dict(model.state_dict())
-            self.target_model.eval()
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return [
-            AveragingNamedMetric("loss"),
-            AveragingNamedMetric("average_q_selected"),
-            AveragingNamedMetric("average_q_target"),
-            AveragingNamedMetric("grad_norm"),
-        ]
-
-
-def create(model: ModelFactory, discount_factor: float, target_update_frequency: int,
-           max_grad_norm: float, double_dqn: bool = False):
-    """ Vel factory function """
-    return DistributionalDeepQLearning(
-        model_factory=model,
-        discount_factor=discount_factor,
-        double_dqn=double_dqn,
-        target_update_frequency=target_update_frequency,
-        max_grad_norm=max_grad_norm
-    )
diff --git a/vel/rl/algo/dqn.py b/vel/rl/algo/dqn.py
deleted file mode 100644
index f3de2eaa..00000000
--- a/vel/rl/algo/dqn.py
+++ /dev/null
@@ -1,102 +0,0 @@
-import torch
-import torch.nn.functional as F
-import torch.nn.utils
-
-from vel.api import ModelFactory
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
-from vel.rl.api import OptimizerAlgoBase
-
-
-class DeepQLearning(OptimizerAlgoBase):
-    """ Deep Q-Learning algorithm """
-
-    def __init__(self, model_factory: ModelFactory, discount_factor: float, double_dqn: bool, target_update_frequency: int,
-                 max_grad_norm: float):
-        super().__init__(max_grad_norm)
-
-        self.model_factory = model_factory
-        self.discount_factor = discount_factor
-
-        self.double_dqn = double_dqn
-        self.target_update_frequency = target_update_frequency
-
-        self.target_model = None
-
-    def initialize(self, training_info, model, environment, device):
-        """ Initialize policy gradient from reinforcer settings """
-        self.target_model = self.model_factory.instantiate(action_space=environment.action_space).to(device)
-        self.target_model.load_state_dict(model.state_dict())
-        self.target_model.eval()
-
-    def calculate_gradient(self, batch_info, device, model, rollout):
-        """ Calculate loss of the supplied rollout """
-        evaluator = model.evaluate(rollout)
-
-        dones_tensor = evaluator.get('rollout:dones')
-        rewards_tensor = evaluator.get('rollout:rewards')
-
-        assert dones_tensor.dtype == torch.float32
-
-        with torch.no_grad():
-            target_evaluator = self.target_model.evaluate(rollout)
-
-            if self.double_dqn:
-                # DOUBLE DQN
-                target_q = target_evaluator.get('model:q_next')
-                model_q = evaluator.get('model:q_next')
-                # Select largest 'target' value based on action that 'model' selects
-                values = target_q.gather(1, model_q.argmax(dim=1, keepdim=True)).squeeze(1)
-            else:
-                # REGULAR DQN
-                # [0] is because in pytorch .max(...) returns tuple (max values, argmax)
-                values = target_evaluator.get('model:q_next').max(dim=1)[0]
-
-            forward_steps = rollout.extra_data.get('forward_steps', 1)
-            estimated_return = rewards_tensor + (self.discount_factor ** forward_steps) * values * (1 - dones_tensor)
-
-        q_selected = evaluator.get('model:action:q')
-
-        if evaluator.is_provided('rollout:weights'):
-            weights = evaluator.get('rollout:weights')
-        else:
-            weights = torch.ones_like(rewards_tensor)
-
-        original_losses = F.smooth_l1_loss(q_selected, estimated_return, reduction='none')
-
-        loss_value = torch.mean(weights * original_losses)
-        loss_value.backward()
-
-        return {
-            'loss': loss_value.item(),
-            # We need it to update priorities in the replay buffer:
-            'errors': original_losses.detach().cpu().numpy(),
-            'average_q_selected': torch.mean(q_selected).item(),
-            'average_q_target': torch.mean(estimated_return).item()
-        }
-
-    def post_optimization_step(self, batch_info, device, model, rollout):
-        """ Steps to take after optimization has been done"""
-        if batch_info.aggregate_batch_number % self.target_update_frequency == 0:
-            self.target_model.load_state_dict(model.state_dict())
-            self.target_model.eval()
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return [
-            AveragingNamedMetric("loss"),
-            AveragingNamedMetric("average_q_selected"),
-            AveragingNamedMetric("average_q_target"),
-            AveragingNamedMetric("grad_norm"),
-        ]
-
-
-def create(model: ModelFactory, discount_factor: float, target_update_frequency: int,
-           max_grad_norm: float, double_dqn: bool=False):
-    """ Vel factory function """
-    return DeepQLearning(
-        model_factory=model,
-        discount_factor=discount_factor,
-        double_dqn=double_dqn,
-        target_update_frequency=target_update_frequency,
-        max_grad_norm=max_grad_norm
-    )
diff --git a/vel/rl/algo/policy_gradient/a2c.py b/vel/rl/algo/policy_gradient/a2c.py
deleted file mode 100644
index 783f954a..00000000
--- a/vel/rl/algo/policy_gradient/a2c.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
-from vel.math.functions import explained_variance
-from vel.rl.api import OptimizerAlgoBase, Rollout, Trajectories
-from vel.rl.discount_bootstrap import discount_bootstrap_gae
-
-
-class A2CPolicyGradient(OptimizerAlgoBase):
-    """ Simplest policy gradient - calculate loss as an advantage of an actor versus value function """
-    def __init__(self, entropy_coefficient, value_coefficient, max_grad_norm, discount_factor: float, gae_lambda=1.0):
-        super().__init__(max_grad_norm)
-
-        self.entropy_coefficient = entropy_coefficient
-        self.value_coefficient = value_coefficient
-        self.gae_lambda = gae_lambda
-        self.discount_factor = discount_factor
-
-    def process_rollout(self, batch_info, rollout: Rollout):
-        """ Process rollout for ALGO before any chunking/shuffling  """
-        assert isinstance(rollout, Trajectories), "A2C requires trajectory rollouts"
-
-        advantages = discount_bootstrap_gae(
-            rewards_buffer=rollout.transition_tensors['rewards'],
-            dones_buffer=rollout.transition_tensors['dones'],
-            values_buffer=rollout.transition_tensors['values'],
-            final_values=rollout.rollout_tensors['final_values'],
-            discount_factor=self.discount_factor,
-            gae_lambda=self.gae_lambda,
-            number_of_steps=rollout.num_steps
-        )
-
-        returns = advantages + rollout.transition_tensors['values']
-
-        rollout.transition_tensors['advantages'] = advantages
-        rollout.transition_tensors['returns'] = returns
-
-        return rollout
-
-    def calculate_gradient(self, batch_info, device, model, rollout):
-        """ Calculate loss of the supplied rollout """
-        evaluator = model.evaluate(rollout)
-
-        # Use evaluator interface to get the what we are interested in from the model
-        advantages = evaluator.get('rollout:advantages')
-        returns = evaluator.get('rollout:returns')
-        rollout_values = evaluator.get('rollout:values')
-
-        logprobs = evaluator.get('model:action:logprobs')
-        values = evaluator.get('model:values')
-        entropy = evaluator.get('model:entropy')
-
-        # Actual calculations. Pretty trivial
-        policy_loss = -torch.mean(advantages * logprobs)
-        value_loss = 0.5 * F.mse_loss(values, returns)
-        policy_entropy = torch.mean(entropy)
-
-        loss_value = (
-            policy_loss - self.entropy_coefficient * policy_entropy + self.value_coefficient * value_loss
-        )
-
-        loss_value.backward()
-
-        return {
-            'policy_loss': policy_loss.item(),
-            'value_loss': value_loss.item(),
-            'policy_entropy': policy_entropy.item(),
-            'advantage_norm': torch.norm(advantages).item(),
-            'explained_variance': explained_variance(returns, rollout_values)
-        }
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return [
-            AveragingNamedMetric("value_loss"),
-            AveragingNamedMetric("policy_entropy"),
-            AveragingNamedMetric("policy_loss"),
-            AveragingNamedMetric("grad_norm"),
-            AveragingNamedMetric("advantage_norm"),
-            AveragingNamedMetric("explained_variance")
-        ]
-
-
-def create(entropy_coefficient, value_coefficient, max_grad_norm, discount_factor, gae_lambda=1.0):
-    """ Vel factory function """
-    return A2CPolicyGradient(
-        entropy_coefficient,
-        value_coefficient,
-        max_grad_norm,
-        discount_factor,
-        gae_lambda
-    )
diff --git a/vel/rl/algo/policy_gradient/ddpg.py b/vel/rl/algo/policy_gradient/ddpg.py
deleted file mode 100644
index 72560f80..00000000
--- a/vel/rl/algo/policy_gradient/ddpg.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import torch
-import typing
-import torch.autograd
-import torch.nn.functional as F
-
-from vel.rl.api import OptimizerAlgoBase
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
-
-
-class DeepDeterministicPolicyGradient(OptimizerAlgoBase):
-    """ Deep Deterministic Policy Gradient (DDPG) - policy gradient calculations """
-
-    def __init__(self, model_factory, discount_factor: float, tau: float, max_grad_norm: typing.Optional[float]=None):
-        super().__init__(max_grad_norm)
-
-        self.model_factory = model_factory
-        self.tau = tau
-        self.discount_factor = discount_factor
-
-        self.target_model = None
-
-    def initialize(self, training_info, model, environment, device):
-        """ Initialize algo from reinforcer settings """
-        self.target_model = self.model_factory.instantiate(action_space=environment.action_space).to(device)
-        self.target_model.load_state_dict(model.state_dict())
-        self.target_model.eval()
-
-    def calculate_gradient(self, batch_info, device, model, rollout):
-        """ Calculate loss of the supplied rollout """
-        rollout = rollout.to_transitions()
-
-        dones = rollout.batch_tensor('dones')
-        rewards = rollout.batch_tensor('rewards')
-        observations_next = rollout.batch_tensor('observations_next')
-        actions = rollout.batch_tensor('actions')
-        observations = rollout.batch_tensor('observations')
-
-        # Calculate value loss - or critic loss
-        with torch.no_grad():
-            target_next_value = self.target_model.value(observations_next)
-            target_value = rewards + (1.0 - dones) * self.discount_factor * target_next_value
-
-        # Value estimation error vs the target network
-        model_value = model.value(observations, actions)
-        value_loss = F.mse_loss(model_value, target_value)
-
-        # It may seem a bit tricky what I'm doing here, but the underlying idea is simple
-        # All other implementations I found keep two separate optimizers for actor and critic
-        # and update them separately
-        # What I'm trying to do is to optimize them both with a single optimizer
-        # but I need to make sure gradients flow correctly
-        # From critic loss to critic network only and from actor loss to actor network only
-
-        # Backpropagate value loss to critic only
-        value_loss.backward()
-
-        model_action = model.action(observations)
-        model_action_value = model.value(observations, model_action)
-
-        policy_loss = -model_action_value.mean()
-
-        model_action_grad = torch.autograd.grad(policy_loss, model_action)[0]
-
-        # Backpropagate actor loss to actor only
-        model_action.backward(gradient=model_action_grad)
-
-        return {
-            'policy_loss': policy_loss.item(),
-            'value_loss': value_loss.item(),
-        }
-
-    def post_optimization_step(self, batch_info, device, model, rollout):
-        """ Steps to take after optimization has been done"""
-        # Update target model
-        for model_param, target_param in zip(model.parameters(), self.target_model.parameters()):
-            # EWMA average model update
-            target_param.data.mul_(1 - self.tau).add_(model_param.data * self.tau)
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return [
-            AveragingNamedMetric("value_loss"),
-            AveragingNamedMetric("policy_loss"),
-        ]
-
-
-def create(model, discount_factor: float, tau: float, max_grad_norm: float=None):
-    """ Vel factory function """
-    return DeepDeterministicPolicyGradient(
-        tau=tau,
-        discount_factor=discount_factor,
-        model_factory=model,
-        max_grad_norm=max_grad_norm
-    )
diff --git a/vel/rl/algo/policy_gradient/ppo.py b/vel/rl/algo/policy_gradient/ppo.py
deleted file mode 100644
index 5f0b3538..00000000
--- a/vel/rl/algo/policy_gradient/ppo.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-
-import numbers
-
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
-from vel.math.functions import explained_variance
-from vel.rl.api import OptimizerAlgoBase, Rollout, Trajectories
-from vel.rl.discount_bootstrap import discount_bootstrap_gae
-from vel.schedules.constant import ConstantSchedule
-
-
-class PpoPolicyGradient(OptimizerAlgoBase):
-    """ Proximal Policy Optimization - https://arxiv.org/abs/1707.06347 """
-    def __init__(self, entropy_coefficient, value_coefficient, cliprange, max_grad_norm, discount_factor: float,
-                 normalize_advantage: bool=True, gae_lambda: float=1.0):
-        super().__init__(max_grad_norm)
-
-        self.entropy_coefficient = entropy_coefficient
-        self.value_coefficient = value_coefficient
-        self.normalize_advantage = normalize_advantage
-
-        if isinstance(cliprange, numbers.Number):
-            self.cliprange = ConstantSchedule(cliprange)
-        else:
-            self.cliprange = cliprange
-
-        self.gae_lambda = gae_lambda
-        self.discount_factor = discount_factor
-
-    def process_rollout(self, batch_info, rollout: Rollout):
-        """ Process rollout for ALGO before any chunking/shuffling  """
-        assert isinstance(rollout, Trajectories), "PPO requires trajectory rollouts"
-
-        advantages = discount_bootstrap_gae(
-            rewards_buffer=rollout.transition_tensors['rewards'],
-            dones_buffer=rollout.transition_tensors['dones'],
-            values_buffer=rollout.transition_tensors['values'],
-            final_values=rollout.rollout_tensors['final_values'],
-            discount_factor=self.discount_factor,
-            gae_lambda=self.gae_lambda,
-            number_of_steps=rollout.num_steps
-        )
-
-        returns = advantages + rollout.transition_tensors['values']
-
-        rollout.transition_tensors['advantages'] = advantages
-        rollout.transition_tensors['returns'] = returns
-
-        return rollout
-
-    def calculate_gradient(self, batch_info, device, model, rollout):
-        """ Calculate loss of the supplied rollout """
-        evaluator = model.evaluate(rollout)
-
-        # Part 0.0 - Rollout values
-        advantages = evaluator.get('rollout:advantages')
-        rollout_values = evaluator.get('rollout:values')
-        rollout_action_logprobs = evaluator.get('rollout:action:logprobs')
-        returns = evaluator.get('rollout:returns')
-
-        # PART 0.1 - Model evaluation
-        entropy = evaluator.get('model:entropy')
-        model_values = evaluator.get('model:values')
-        model_action_logprobs = evaluator.get('model:action:logprobs')
-
-        # Select the cliprange
-        current_cliprange = self.cliprange.value(batch_info['progress'])
-
-        # Normalize the advantages?
-        if self.normalize_advantage:
-            advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
-
-        # PART 1 - policy entropy
-        policy_entropy = torch.mean(entropy)
-
-        # PART 2 - value function
-        value_output_clipped = rollout_values + torch.clamp(
-            model_values - rollout_values, -current_cliprange, current_cliprange
-        )
-        value_loss_part1 = (model_values - returns).pow(2)
-        value_loss_part2 = (value_output_clipped - returns).pow(2)
-        value_loss = 0.5 * torch.mean(torch.max(value_loss_part1, value_loss_part2))
-
-        # PART 3 - policy gradient loss
-        ratio = torch.exp(model_action_logprobs - rollout_action_logprobs)
-
-        pg_loss_part1 = -advantages * ratio
-        pg_loss_part2 = -advantages * torch.clamp(ratio, 1.0 - current_cliprange, 1.0 + current_cliprange)
-        policy_loss = torch.mean(torch.max(pg_loss_part1, pg_loss_part2))
-
-        loss_value = (
-            policy_loss - self.entropy_coefficient * policy_entropy + self.value_coefficient * value_loss
-        )
-
-        loss_value.backward()
-
-        with torch.no_grad():
-            approx_kl_divergence = 0.5 * torch.mean((model_action_logprobs - rollout_action_logprobs).pow(2))
-            clip_fraction = torch.mean((torch.abs(ratio - 1.0) > current_cliprange).to(dtype=torch.float))
-
-        return {
-            'policy_loss': policy_loss.item(),
-            'value_loss': value_loss.item(),
-            'policy_entropy': policy_entropy.item(),
-            'approx_kl_divergence': approx_kl_divergence.item(),
-            'clip_fraction': clip_fraction.item(),
-            'advantage_norm': torch.norm(advantages).item(),
-            'explained_variance': explained_variance(returns, rollout_values)
-        }
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return [
-            AveragingNamedMetric("policy_loss"),
-            AveragingNamedMetric("value_loss"),
-            AveragingNamedMetric("policy_entropy"),
-            AveragingNamedMetric("approx_kl_divergence"),
-            AveragingNamedMetric("clip_fraction"),
-            AveragingNamedMetric("grad_norm"),
-            AveragingNamedMetric("advantage_norm"),
-            AveragingNamedMetric("explained_variance")
-        ]
-
-
-def create(entropy_coefficient, value_coefficient, cliprange, max_grad_norm, discount_factor,
-           normalize_advantage=True, gae_lambda=1.0):
-    """ Vel factory function """
-    return PpoPolicyGradient(
-        entropy_coefficient, value_coefficient, cliprange, max_grad_norm,
-        discount_factor=discount_factor,
-        normalize_advantage=normalize_advantage,
-        gae_lambda=gae_lambda
-    )
diff --git a/vel/rl/algo/policy_gradient/trpo.py b/vel/rl/algo/policy_gradient/trpo.py
deleted file mode 100644
index b73f3a67..00000000
--- a/vel/rl/algo/policy_gradient/trpo.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import numpy as np
-import torch
-import torch.autograd as autograd
-import torch.nn.functional as F
-import torch.nn.utils
-
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
-from vel.math.functions import explained_variance
-from vel.rl.api import AlgoBase, Rollout, Trajectories
-from vel.rl.discount_bootstrap import discount_bootstrap_gae
-
-
-def p2v(params):
-    """ Parameters to vector - shorthand utility version """
-    return torch.nn.utils.parameters_to_vector(params)
-
-
-def v2p(vector, params):
-    """ Vector to parameters - shorthand utility version """
-    return torch.nn.utils.vector_to_parameters(vector, params)
-
-
-def conjugate_gradient_method(matrix_vector_operator, loss_gradient, nsteps, rdotr_tol=1e-10):
-    """ Conjugate gradient algorithm """
-    x = torch.zeros_like(loss_gradient)
-
-    r = loss_gradient.clone()
-    p = loss_gradient.clone()
-
-    rdotr = torch.dot(r, r)
-
-    for i in range(nsteps):
-        Avp = matrix_vector_operator(p)
-        alpha = rdotr / torch.dot(p, Avp)
-
-        x += alpha * p
-        r -= alpha * Avp
-
-        new_rdotr = torch.dot(r, r)
-        betta = new_rdotr / rdotr
-        p = r + betta * p
-        rdotr = new_rdotr
-
-        if rdotr < rdotr_tol:
-            break
-
-    return x
-
-
-class TrpoPolicyGradient(AlgoBase):
-    """ Trust Region Policy Optimization - https://arxiv.org/abs/1502.05477 """
-
-    def __init__(self, max_kl, cg_iters, line_search_iters, cg_damping, entropy_coef, vf_iters,
-                 discount_factor, gae_lambda, improvement_acceptance_ratio, max_grad_norm):
-        self.mak_kl = max_kl
-        self.cg_iters = cg_iters
-        self.line_search_iters = line_search_iters
-        self.cg_damping = cg_damping
-        self.entropy_coef = entropy_coef
-        self.vf_iters = vf_iters
-        self.discount_factor = discount_factor
-        self.gae_lambda = gae_lambda
-        self.improvement_acceptance_ratio = improvement_acceptance_ratio
-        self.max_grad_norm = max_grad_norm
-
-    def process_rollout(self, batch_info, rollout: Rollout):
-        """ Process rollout for ALGO before any chunking/shuffling  """
-        assert isinstance(rollout, Trajectories), "TRPO requires trajectory rollouts"
-
-        advantages = discount_bootstrap_gae(
-            rewards_buffer=rollout.transition_tensors['rewards'],
-            dones_buffer=rollout.transition_tensors['dones'],
-            values_buffer=rollout.transition_tensors['values'],
-            final_values=rollout.rollout_tensors['final_values'],
-            discount_factor=self.discount_factor,
-            gae_lambda=self.gae_lambda,
-            number_of_steps=rollout.num_steps
-        )
-
-        returns = advantages + rollout.transition_tensors['values']
-
-        rollout.transition_tensors['advantages'] = advantages
-        rollout.transition_tensors['returns'] = returns
-
-        return rollout
-
-    def optimizer_step(self, batch_info, device, model, rollout):
-        """ Single optimization step for a model """
-        rollout = rollout.to_transitions()
-
-        # This algorithm makes quote strong assumptions about how does the model look
-        # so it does not make that much sense to switch to the evaluator interface
-        # As it would be more of a problem than actual benefit
-
-        observations = rollout.batch_tensor('observations')
-        returns = rollout.batch_tensor('returns')
-
-        # Evaluate model on the observations
-        policy_params = model.policy(observations)
-        policy_entropy = torch.mean(model.entropy(policy_params))
-
-        policy_loss = self.calc_policy_loss(model, policy_params, policy_entropy, rollout)
-        policy_grad = p2v(autograd.grad(policy_loss, model.policy_parameters(), retain_graph=True)).detach()
-
-        # Calculate gradient of KL divergence of model with fixed version of itself
-        # Value of kl_divergence will be 0, but what we need is the gradient, actually the 2nd derivarive
-        kl_divergence = torch.mean(model.kl_divergence(policy_params.detach(), policy_params))
-        kl_divergence_gradient = p2v(torch.autograd.grad(kl_divergence, model.policy_parameters(), create_graph=True))
-
-        step_direction = conjugate_gradient_method(
-            matrix_vector_operator=lambda x: self.fisher_vector_product(x, kl_divergence_gradient, model),
-            # Because we want to decrease the loss, we want to go into the direction of -gradient
-            loss_gradient=-policy_grad,
-            nsteps=self.cg_iters
-        )
-
-        shs = 0.5 * step_direction @ self.fisher_vector_product(step_direction, kl_divergence_gradient, model)
-        lm = torch.sqrt(shs / self.mak_kl)
-        full_step = step_direction / lm
-
-        # Because we want to decrease the loss, we want to go into the direction of -gradient
-        expected_improvement = (-policy_grad) @ full_step
-        original_parameter_vec = p2v(model.policy_parameters()).detach_()
-
-        policy_optimization_success, ratio, policy_loss_improvement, new_policy_loss, kl_divergence_step = self.line_search(
-            model, rollout, policy_loss, policy_params, original_parameter_vec, full_step, expected_improvement
-        )
-
-        gradient_norms = []
-
-        for i in range(self.vf_iters):
-            batch_info.optimizer.zero_grad()
-            value_loss = self.value_loss(model, observations, returns)
-
-            value_loss.backward()
-
-            # Gradient clipping
-            if self.max_grad_norm is not None:
-                grad_norm = torch.nn.utils.clip_grad_norm_(
-                    filter(lambda p: p.requires_grad, model.parameters()),
-                    max_norm=self.max_grad_norm
-                )
-
-                gradient_norms.append(grad_norm)
-
-            batch_info.optimizer.step(closure=None)
-
-        if gradient_norms:
-            gradient_norm = np.mean(gradient_norms)
-        else:
-            gradient_norm = 0.0
-
-        # noinspection PyUnboundLocalVariable
-        return {
-            'new_policy_loss': new_policy_loss.item(),
-            'policy_entropy': policy_entropy.item(),
-            'value_loss': value_loss.item(),
-            'policy_optimization_success': float(policy_optimization_success),
-            'policy_improvement_ratio': ratio.item(),
-            'kl_divergence_step': kl_divergence_step.item(),
-            'policy_loss_improvement': policy_loss_improvement.item(),
-            'grad_norm': gradient_norm,
-            'advantage_norm': torch.norm(rollout.batch_tensor('advantages')).item(),
-            'explained_variance': explained_variance(returns, rollout.batch_tensor('values'))
-        }
-
-    def line_search(self, model, rollout, original_policy_loss, original_policy_params, original_parameter_vec,
-                    full_step, expected_improvement_full):
-        """ Find the right stepsize to make sure policy improves """
-        current_parameter_vec = original_parameter_vec.clone()
-
-        for idx in range(self.line_search_iters):
-            stepsize = 0.5 ** idx
-
-            new_parameter_vec = current_parameter_vec + stepsize * full_step
-
-            # Update model parameters
-            v2p(new_parameter_vec, model.policy_parameters())
-
-            # Calculate new loss
-            with torch.no_grad():
-                policy_params = model.policy(rollout.batch_tensor('observations'))
-                policy_entropy = torch.mean(model.entropy(policy_params))
-                kl_divergence = torch.mean(model.kl_divergence(original_policy_params, policy_params))
-
-                new_loss = self.calc_policy_loss(model, policy_params, policy_entropy, rollout)
-
-                actual_improvement = original_policy_loss - new_loss
-                expected_improvement = expected_improvement_full * stepsize
-
-                ratio = actual_improvement / expected_improvement
-
-            if kl_divergence.item() > self.mak_kl * 1.5:
-                # KL divergence bound exceeded
-                continue
-            elif ratio < expected_improvement:
-                # Not enough loss improvement
-                continue
-            else:
-                # Optimization successful
-                return True, ratio, actual_improvement, new_loss, kl_divergence
-
-        # Optimization failed, revert to initial parameters
-        v2p(original_parameter_vec, model.policy_parameters())
-        return False, torch.tensor(0.0), torch.tensor(0.0), torch.tensor(0.0), torch.tensor(0.0)
-
-    def fisher_vector_product(self, vector, kl_divergence_gradient, model):
-        """ Calculate product Hessian @ vector """
-        assert not vector.requires_grad, "Vector must not propagate gradient"
-        dot_product = vector @ kl_divergence_gradient
-
-        # at least one dimension spans across two contiguous subspaces
-        double_gradient = torch.autograd.grad(dot_product, model.policy_parameters(), retain_graph=True)
-        fvp = p2v(x.contiguous() for x in double_gradient)
-
-        return fvp + vector * self.cg_damping
-
-    def value_loss(self, model, observations, discounted_rewards):
-        """ Loss of value estimator """
-        value_outputs = model.value(observations)
-        value_loss = 0.5 * F.mse_loss(value_outputs, discounted_rewards)
-        return value_loss
-
-    def calc_policy_loss(self, model, policy_params, policy_entropy, rollout):
-        """
-        Policy gradient loss - calculate from probability distribution
-
-        Calculate surrogate loss - advantage * policy_probability / fixed_initial_policy_probability
-
-        Because we operate with logarithm of -probability (neglogp) we do
-        - advantage * exp(fixed_neglogps - model_neglogps)
-        """
-        actions = rollout.batch_tensor('actions')
-        advantages = rollout.batch_tensor('advantages')
-        fixed_logprobs = rollout.batch_tensor('action:logprobs')
-
-        model_logprobs = model.logprob(actions, policy_params)
-
-        # Normalize advantages
-        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
-
-        # We put - in front because we want to maximize the surrogate objective
-        policy_loss = -advantages * torch.exp(model_logprobs - fixed_logprobs)
-
-        return policy_loss.mean() - policy_entropy * self.entropy_coef
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return [
-            AveragingNamedMetric("new_policy_loss"),
-            AveragingNamedMetric("policy_entropy"),
-            AveragingNamedMetric("value_loss"),
-            AveragingNamedMetric("policy_optimization_success"),
-            AveragingNamedMetric("policy_improvement_ratio"),
-            AveragingNamedMetric("kl_divergence_step"),
-            AveragingNamedMetric("policy_loss_improvement"),
-            AveragingNamedMetric("grad_norm"),
-            AveragingNamedMetric("advantage_norm"),
-            AveragingNamedMetric("explained_variance")
-        ]
-
-
-def create(max_kl, cg_iters, line_search_iters, cg_damping, entropy_coef, vf_iters, discount_factor,
-           gae_lambda=1.0, improvement_acceptance_ratio=0.1, max_grad_norm=0.5):
-    """ Vel factory function """
-    return TrpoPolicyGradient(
-        max_kl, int(cg_iters), int(line_search_iters), cg_damping, entropy_coef, vf_iters,
-        discount_factor=discount_factor,
-        gae_lambda=gae_lambda,
-        improvement_acceptance_ratio=improvement_acceptance_ratio,
-        max_grad_norm=max_grad_norm
-    )
diff --git a/vel/rl/api/__init__.py b/vel/rl/api/__init__.py
index a48ee85a..8102ab03 100644
--- a/vel/rl/api/__init__.py
+++ b/vel/rl/api/__init__.py
@@ -1,8 +1,6 @@
-from .algo_base import AlgoBase, OptimizerAlgoBase
 from .env_base import EnvFactory, VecEnvFactory
 from .env_roller import EnvRollerBase, ReplayEnvRollerBase, EnvRollerFactoryBase, ReplayEnvRollerFactoryBase
-from .evaluator import Evaluator
-from .model import RlModel, RlRnnModel
-from .reinforcer_base import ReinforcerBase, ReinforcerFactory
-from .replay_buffer import ReplayBuffer, ReplayBufferFactory
 from .rollout import Rollout, Trajectories, Transitions
+from .policy import RlPolicy
+from .reinforcer_base import Reinforcer, ReinforcerFactory
+from .replay_buffer import ReplayBuffer, ReplayBufferFactory
diff --git a/vel/rl/api/algo_base.py b/vel/rl/api/algo_base.py
deleted file mode 100644
index d9e6c8c4..00000000
--- a/vel/rl/api/algo_base.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import torch.nn.utils
-
-
-def clip_gradients(batch_result, model, max_grad_norm):
-    """ Clip gradients to a given maximum length """
-    if max_grad_norm is not None:
-        grad_norm = torch.nn.utils.clip_grad_norm_(
-            filter(lambda p: p.requires_grad, model.parameters()),
-            max_norm=max_grad_norm
-        )
-    else:
-        grad_norm = 0.0
-
-    batch_result['grad_norm'] = grad_norm
-
-
-class AlgoBase:
-    """ Base class for algo reinforcement calculations """
-
-    def initialize(self, training_info, model, environment, device):
-        """ Initialize algo from reinforcer settings """
-        pass
-
-    def process_rollout(self, batch_info, rollout):
-        """ Process rollout for ALGO before any chunking/shuffling  """
-        return rollout
-
-    def optimizer_step(self, batch_info, device, model, rollout):
-        """ Single optimization step for a model """
-        raise NotImplementedError
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return []
-
-
-class OptimizerAlgoBase(AlgoBase):
-    """ RL algo that does a simple optimizer update """
-
-    def __init__(self, max_grad_norm):
-        self.max_grad_norm = max_grad_norm
-
-    def calculate_gradient(self, batch_info, device, model, rollout):
-        """ Calculate loss of the supplied rollout """
-        raise NotImplementedError
-
-    def post_optimization_step(self, batch_info, device, model, rollout):
-        """ Steps to take after optimization has been done"""
-        pass
-
-    def optimizer_step(self, batch_info, device, model, rollout):
-        """ Single optimization step for a model """
-        batch_info.optimizer.zero_grad()
-
-        batch_result = self.calculate_gradient(batch_info=batch_info, device=device, model=model, rollout=rollout)
-
-        clip_gradients(batch_result, model, self.max_grad_norm)
-
-        batch_info.optimizer.step(closure=None)
-
-        self.post_optimization_step(batch_info, device, model, rollout)
-
-        return batch_result
-
-    def metrics(self) -> list:
-        """ List of metrics to track for this learning process """
-        return []
diff --git a/vel/rl/api/env_base.py b/vel/rl/api/env_base.py
index 07e0be1a..eb0c6fba 100644
--- a/vel/rl/api/env_base.py
+++ b/vel/rl/api/env_base.py
@@ -25,4 +25,3 @@ def instantiate(self, parallel_envs, seed=0, preset='default') -> VecEnv:
     def instantiate_single(self, seed=0, preset='default') -> VecEnv:
         """ Create a new VecEnv instance - single """
         raise NotImplementedError
-
diff --git a/vel/rl/api/env_roller.py b/vel/rl/api/env_roller.py
index 3457104d..f868fe90 100644
--- a/vel/rl/api/env_roller.py
+++ b/vel/rl/api/env_roller.py
@@ -1,20 +1,19 @@
 import typing
-import gym
 
-from vel.rl.api.rollout import Rollout
-from vel.api import BatchInfo, Model
+from vel.api import BatchInfo
 from vel.openai.baselines.common.vec_env import VecEnv
+from vel.rl.api.rollout import Rollout
 
 
 class EnvRollerBase:
     """ Class generating environment rollouts """
 
     @property
-    def environment(self) -> typing.Union[gym.Env, VecEnv]:
+    def environment(self) -> VecEnv:
         """ Reference to environment being evaluated """
         raise NotImplementedError
 
-    def rollout(self, batch_info: BatchInfo, model: Model, number_of_steps: int) -> Rollout:
+    def rollout(self, batch_info: BatchInfo, number_of_steps: int) -> Rollout:
         """ Roll-out the environment and return it """
         raise NotImplementedError
 
@@ -27,7 +26,7 @@ def metrics(self) -> list:
 class ReplayEnvRollerBase(EnvRollerBase):
     """ Class generating environment rollouts with experience replay """
 
-    def sample(self, batch_info: BatchInfo, model: Model, number_of_steps: int) -> Rollout:
+    def sample(self, batch_info: BatchInfo, number_of_steps: int) -> Rollout:
         """ Sample experience from replay buffer and return a batch """
         raise NotImplementedError
 
@@ -47,7 +46,7 @@ def update(self, rollout, batch_info):
 class EnvRollerFactoryBase:
     """ Factory for env rollers """
 
-    def instantiate(self, environment, device) -> EnvRollerBase:
+    def instantiate(self, environment, policy, device) -> EnvRollerBase:
         """ Instantiate env roller """
         raise NotImplementedError
 
@@ -55,6 +54,6 @@ def instantiate(self, environment, device) -> EnvRollerBase:
 class ReplayEnvRollerFactoryBase(EnvRollerFactoryBase):
     """ Factory for env rollers """
 
-    def instantiate(self, environment, device) -> ReplayEnvRollerBase:
+    def instantiate(self, environment, policy, device) -> ReplayEnvRollerBase:
         """ Instantiate env roller """
         raise NotImplementedError
diff --git a/vel/rl/api/evaluator.py b/vel/rl/api/evaluator.py
deleted file mode 100644
index dd5cb9ec..00000000
--- a/vel/rl/api/evaluator.py
+++ /dev/null
@@ -1,137 +0,0 @@
-class EvaluatorMeta(type):
-    """ Metaclass for Evaluator - gathers all provider methods in a class attribute """
-    def __new__(mcs, name, bases, attributes):
-        providers = {}
-
-        for name, attr in attributes.items():
-            if callable(attr):
-                proper_name = getattr(attr, '_vel_evaluator_provides', None)
-
-                if proper_name is not None:
-                    providers[proper_name] = attr
-
-        attributes['_providers'] = providers
-
-        return super().__new__(mcs, name, bases, attributes)
-
-
-class Evaluator(metaclass=EvaluatorMeta):
-    """
-    Different models may have different outputs and approach evaluating environment differently.
-
-    Evaluator is an object that abstracts over that, providing unified interface between algorithms
-    which just need certain outputs from models and models that may provide them in different ways.
-
-    I'll try to maintain here a dictionary of possible common values that can be requested from the evaluator.
-    Rollouts should communicate using the same names
-
-    - rollout:estimated_returns
-        - Bootstrapped return (sum of discounted future rewards) estimated using returns and value estimates
-    - rollout:values
-        - Value estimates from the model that was used to generate the rollout
-    - rollout:estimated_advantages
-        - Advantage of a rollout (state, action) pair by the model that was used to generate the rollout
-    - rollout:actions
-        - Actions performed in a rollout
-    - rollout:logprobs
-        - Logarithm of probability for **all** actions of a policy used to perform rollout
-        (defined only for finite action spaces)
-    - rollout:action:logprobs
-        - Logarithm of probability only for selected actions
-    - rollout:dones
-        - Whether given observation is last in a trajectory
-    - rollout:dones
-        - Raw rewards received from the environment in this learning process
-    - rollout:final_values
-        - Value estimates for observation after final observation in the rollout
-    - rollout:observations
-        - Observations of the rollout
-    - rollout:observations_next
-        - Next observations in the rollout
-    - rollout:weights
-        - Error weights of rollout samples
-    - rollout:q
-        - Action-values for each action in current space
-        (defined only for finite action spaces)
-
-    - model:logprobs
-        - Logarithm of probability of **all** actions in an environment as in current model policy
-        (defined only for finite action spaces)
-    - model:q
-        - Action-value for **all** actions
-        (defined only for finite action spaces)
-    - model:q_dist
-        - Action-value histogram for **all** actions
-        (defined only for finite action spaces)
-    - model:q_dist_next
-        - Action-value histogram for **all** actions from the 'next' state in the rollout
-        (defined only for finite action spaces)
-    - model:q_next
-        - Action-value for **all** actions from the 'next' state in the rollout
-        (defined only for finite action spaces)
-    - model:entropy
-        - Policy entropy for selected states
-    - model:action:q
-        - Action-value for actions selected in the rollout
-    - model:model_action:q
-        - Action-value for actions that model would perform (Deterministic policy only)
-    - model:actions
-        - Actions that model would perform (Deterministic policy only)
-    - model:action:logprobs
-        - Logarithm of probability for performed actions
-    - model:policy_params
-        - Parametrizations of policy for each state
-    - model:values
-        - Value estimates for each state, estimated by the current model
-    - model:values_next
-        - Value estimates for 'next' state of each transition
-    """
-
-    @staticmethod
-    def provides(name):
-        """ Function decorator - value provided by the evaluator """
-        def decorator(func):
-            func._vel_evaluator_provides = name
-            return func
-
-        return decorator
-
-    def __init__(self, rollout):
-        self._storage = {}
-        self.rollout = rollout
-
-    def is_provided(self, name):
-        """ Capability check if evaluator provides given value """
-        if name in self._storage:
-            return True
-        elif name in self._providers:
-            return True
-        elif name.startswith('rollout:'):
-            rollout_name = name[8:]
-        else:
-            return False
-
-    def get(self, name):
-        """
-        Return a value from this evaluator.
-
-        Because tensor calculated is cached, it may lead to suble bugs if the same value is used multiple times
-        with and without no_grad() context.
-
-        It is advised in such cases to not use no_grad and stick to .detach()
-        """
-        if name in self._storage:
-            return self._storage[name]
-        elif name in self._providers:
-            value = self._storage[name] = self._providers[name](self)
-            return value
-        elif name.startswith('rollout:'):
-            rollout_name = name[8:]
-            value = self._storage[name] = self.rollout.batch_tensor(rollout_name)
-            return value
-        else:
-            raise RuntimeError(f"Key {name} is not provided by this evaluator")
-
-    def provide(self, name, value):
-        """ Provide given value under specified name """
-        self._storage[name] = value
diff --git a/vel/rl/api/model.py b/vel/rl/api/model.py
deleted file mode 100644
index c2d02da2..00000000
--- a/vel/rl/api/model.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-
-from vel.api import Model
-
-from .rollout import Rollout
-from .evaluator import Evaluator
-
-
-class RlModel(Model):
-    """ Reinforcement learning model """
-
-    def step(self, observations) -> dict:
-        """
-        Evaluate environment on given observations, return actions and potentially some extra information
-        in a dictionary.
-        """
-        raise NotImplementedError
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        raise NotImplementedError
-
-
-class RlRnnModel(Model):
-    """ Reinforcement learning recurrent model """
-
-    @property
-    def is_recurrent(self) -> bool:
-        """ If the network is recurrent and needs to be fed previous state """
-        return True
-
-    def step(self, observations, state) -> dict:
-        """
-        Evaluate environment on given observations, return actions and potentially some extra information
-        in a dictionary.
-        """
-        raise NotImplementedError
-
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
-        raise NotImplementedError
-
-    def zero_state(self, batch_size):
-        """ Initial state of the network """
-        return torch.zeros(batch_size, self.state_dim)
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        raise NotImplementedError
diff --git a/vel/rl/api/policy.py b/vel/rl/api/policy.py
new file mode 100644
index 00000000..b69813f0
--- /dev/null
+++ b/vel/rl/api/policy.py
@@ -0,0 +1,77 @@
+import torch
+
+from vel.api import OptimizedModel, VelOptimizer, OptimizerFactory, BatchInfo
+from vel.rl.api import Rollout
+
+
+class RlPolicy(OptimizedModel):
+    """ Base class for reinforcement learning policies """
+
+    def __init__(self, discount_factor: float):
+        super().__init__()
+
+        self.discount_factor = discount_factor
+
+    def process_rollout(self, rollout: Rollout) -> Rollout:
+        """ Process rollout for optimization before any chunking/shuffling  """
+        return rollout
+
+    def act(self, observation, state=None, deterministic=False) -> dict:
+        """
+        Make an action based on the observation from the environment.
+        Returned dictionary must have 'actions' key that contains an action per
+        each env in the observations
+        """
+        raise NotImplementedError
+
+    def reset_episodic_state(self, dones: torch.Tensor):
+        """ Called by the rollout worker, whenever episode is finished """
+        pass
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        parameters = filter(lambda p: p.requires_grad, self.parameters())
+        return optimizer_factory.instantiate(parameters)
+
+    def optimize(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """
+        Perform one step of optimization of the policy based on provided rollout data
+        :returns a dictionary of metrics
+        """
+        batch_info.optimizer.zero_grad()
+
+        metrics = self.calculate_gradient(batch_info, rollout)
+
+        opt_metrics = batch_info.optimizer.step()
+
+        for key, value in opt_metrics.items():
+            metrics[key] = value
+
+        self.post_optimization_step(batch_info, rollout)
+
+        return metrics
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """
+        Calculate gradient for given batch of training data.
+        :returns a dictionary of metrics
+        """
+        raise NotImplementedError
+
+    def post_optimization_step(self, batch_info: BatchInfo, rollout: Rollout):
+        """ Optional operations to perform after optimization """
+        pass
+
+    def reset_state(self, state, dones):
+        """ Reset the state after the episode has been terminated """
+        raise NotImplementedError
+
+    ####################################################################################################################
+    # Utility Methods - that provide default implementations but may be short circuited by some implementations
+    def action(self, observation, state=None, deterministic=False):
+        """ Return policy action for given observation """
+        return self.act(observation, state=state, deterministic=deterministic)['actions']
+
+    def value(self, observation, state=None, deterministic=False):
+        """ Return policy action for given observation """
+        return self.act(observation, state=state, deterministic=deterministic)['values']
diff --git a/vel/rl/api/reinforcer_base.py b/vel/rl/api/reinforcer_base.py
index 1db2ed34..3f9e7c66 100644
--- a/vel/rl/api/reinforcer_base.py
+++ b/vel/rl/api/reinforcer_base.py
@@ -1,9 +1,10 @@
 import torch
 
-from vel.api import TrainingInfo, EpochInfo, BatchInfo, Model
+from vel.api import TrainingInfo, EpochInfo, BatchInfo
+from vel.rl.api import RlPolicy
 
 
-class ReinforcerBase:
+class Reinforcer:
     """
     Manages training process of a single model.
     Learner version for reinforcement-learning problems.
@@ -26,13 +27,13 @@ def metrics(self) -> list:
         raise NotImplementedError
 
     @property
-    def model(self) -> Model:
+    def policy(self) -> RlPolicy:
         """ Model trained by this reinforcer """
         raise NotImplementedError
 
 
 class ReinforcerFactory:
     """ A reinforcer factory """
-    def instantiate(self, device: torch.device) -> ReinforcerBase:
+    def instantiate(self, device: torch.device) -> Reinforcer:
         """ Create new reinforcer instance """
         raise NotImplementedError
diff --git a/vel/rl/api/rollout.py b/vel/rl/api/rollout.py
index 01720b97..4e7695d7 100644
--- a/vel/rl/api/rollout.py
+++ b/vel/rl/api/rollout.py
@@ -100,7 +100,8 @@ class Trajectories(Rollout):
     transition_tensors - tensors that have a row (multidimensional) per each transition. E.g. state, reward, done
     rollout_tensors - tensors that have a row (multidimensional) per whole rollout. E.g. final_value, initial rnn state
     """
-    def __init__(self, num_steps, num_envs, environment_information, transition_tensors, rollout_tensors, extra_data=None):
+    def __init__(self, num_steps, num_envs, environment_information, transition_tensors, rollout_tensors,
+                 extra_data=None):
         self.num_steps = num_steps
         self.num_envs = num_envs
         self.environment_information = environment_information
@@ -111,11 +112,15 @@ def __init__(self, num_steps, num_envs, environment_information, transition_tens
     def to_transitions(self) -> 'Transitions':
         """ Convert given rollout to Transitions """
         # No need to propagate 'rollout_tensors' as they won't mean anything
+
+        if self.environment_information is not None:
+            env_info = [ei for l in self.environment_information for ei in l]
+        else:
+            env_info = None
+
         return Transitions(
             size=self.num_steps * self.num_envs,
-            environment_information=
-                [ei for l in self.environment_information for ei in l]
-                if self.environment_information is not None else None,
+            environment_information=env_info,
             transition_tensors={
                 name: tensor_util.merge_first_two_dims(t) for name, t in self.transition_tensors.items()
             },
@@ -171,7 +176,7 @@ def frames(self):
         """ Number of frames in rollout """
         return self.num_steps * self.num_envs
 
-    def to_device(self, device, non_blocking=True):
+    def to_device(self, device, non_blocking=False):
         """ Move a rollout to a selected device """
         return Trajectories(
             num_steps=self.num_steps,
diff --git a/vel/rl/test/__init__.py b/vel/rl/buffer/__init__.py
similarity index 100%
rename from vel/rl/test/__init__.py
rename to vel/rl/buffer/__init__.py
diff --git a/vel/schedules/__init__.py b/vel/rl/buffer/backend/__init__.py
similarity index 100%
rename from vel/schedules/__init__.py
rename to vel/rl/buffer/backend/__init__.py
diff --git a/vel/rl/buffers/backend/circular_buffer_backend.py b/vel/rl/buffer/backend/circular_buffer_backend.py
similarity index 99%
rename from vel/rl/buffers/backend/circular_buffer_backend.py
rename to vel/rl/buffer/backend/circular_buffer_backend.py
index a328d7fa..7be43246 100644
--- a/vel/rl/buffers/backend/circular_buffer_backend.py
+++ b/vel/rl/buffer/backend/circular_buffer_backend.py
@@ -1,7 +1,7 @@
 import gym
 import numpy as np
 
-from vel.exceptions import VelException
+from vel.exception import VelException
 
 
 class CircularBufferBackend:
diff --git a/vel/rl/buffers/backend/circular_vec_buffer_backend.py b/vel/rl/buffer/backend/circular_vec_buffer_backend.py
similarity index 99%
rename from vel/rl/buffers/backend/circular_vec_buffer_backend.py
rename to vel/rl/buffer/backend/circular_vec_buffer_backend.py
index 074700bc..3b30fb6f 100644
--- a/vel/rl/buffers/backend/circular_vec_buffer_backend.py
+++ b/vel/rl/buffer/backend/circular_vec_buffer_backend.py
@@ -1,7 +1,7 @@
 import gym
 import numpy as np
 
-from vel.exceptions import VelException
+from vel.exception import VelException
 
 
 def take_along_axis(large_array, indexes):
@@ -22,7 +22,7 @@ class CircularVecEnvBufferBackend:
     """
 
     def __init__(self, buffer_capacity: int, num_envs: int, observation_space: gym.Space, action_space: gym.Space,
-                 frame_stack_compensation: bool=False, frame_history: int=1):
+                 frame_stack_compensation: bool = False, frame_history: int = 1):
         # Maximum number of items in the buffer
         self.buffer_capacity = buffer_capacity
 
diff --git a/vel/rl/buffers/backend/prioritized_buffer_backend.py b/vel/rl/buffer/backend/prioritized_buffer_backend.py
similarity index 100%
rename from vel/rl/buffers/backend/prioritized_buffer_backend.py
rename to vel/rl/buffer/backend/prioritized_buffer_backend.py
diff --git a/vel/rl/buffers/backend/prioritized_vec_buffer_backend.py b/vel/rl/buffer/backend/prioritized_vec_buffer_backend.py
similarity index 100%
rename from vel/rl/buffers/backend/prioritized_vec_buffer_backend.py
rename to vel/rl/buffer/backend/prioritized_vec_buffer_backend.py
diff --git a/vel/rl/buffers/backend/segment_tree.py b/vel/rl/buffer/backend/segment_tree.py
similarity index 100%
rename from vel/rl/buffers/backend/segment_tree.py
rename to vel/rl/buffer/backend/segment_tree.py
diff --git a/vel/rl/buffers/circular_replay_buffer.py b/vel/rl/buffer/circular_replay_buffer.py
similarity index 98%
rename from vel/rl/buffers/circular_replay_buffer.py
rename to vel/rl/buffer/circular_replay_buffer.py
index 6595e0c5..b5eed39a 100644
--- a/vel/rl/buffers/circular_replay_buffer.py
+++ b/vel/rl/buffer/circular_replay_buffer.py
@@ -35,7 +35,7 @@ def is_ready_for_sampling(self) -> bool:
 
     def initial_memory_size_hint(self) -> typing.Optional[int]:
         """ Hint how much data is needed to begin sampling, required only for diagnostics """
-        return self.buffer_initial_size
+        return self.buffer_initial_size * self.backend.num_envs
 
     def _get_transitions(self, indexes):
         """ Return batch with given indexes """
diff --git a/vel/rl/buffers/prioritized_circular_replay_buffer.py b/vel/rl/buffer/prioritized_circular_replay_buffer.py
similarity index 99%
rename from vel/rl/buffers/prioritized_circular_replay_buffer.py
rename to vel/rl/buffer/prioritized_circular_replay_buffer.py
index 85f6dedb..0081d73c 100644
--- a/vel/rl/buffers/prioritized_circular_replay_buffer.py
+++ b/vel/rl/buffer/prioritized_circular_replay_buffer.py
@@ -58,7 +58,7 @@ def _get_transitions(self, probs, indexes, tree_idxs, batch_info, forward_steps=
         weights = (capacity * probs) ** (-priority_weight)
         weights = weights / weights.max(axis=0, keepdims=True)
 
-        transition_arrays['weights'] = weights
+        transition_arrays['weights'] = weights.astype(np.float32)
         transition_tensors = {k: torch.from_numpy(v) for k, v in transition_arrays.items()}
 
         transitions = Trajectories(
diff --git a/vel/sources/__init__.py b/vel/rl/buffer/test/__init__.py
similarity index 100%
rename from vel/sources/__init__.py
rename to vel/rl/buffer/test/__init__.py
diff --git a/vel/rl/buffers/tests/test_circular_buffer_backend.py b/vel/rl/buffer/test/test_circular_buffer_backend.py
similarity index 99%
rename from vel/rl/buffers/tests/test_circular_buffer_backend.py
rename to vel/rl/buffer/test/test_circular_buffer_backend.py
index 53031de8..6cf7c9fc 100644
--- a/vel/rl/buffers/tests/test_circular_buffer_backend.py
+++ b/vel/rl/buffer/test/test_circular_buffer_backend.py
@@ -4,8 +4,8 @@
 import numpy.testing as nt
 import pytest
 
-from vel.exceptions import VelException
-from vel.rl.buffers.backend.circular_buffer_backend import CircularBufferBackend
+from vel.exception import VelException
+from vel.rl.buffer.backend.circular_buffer_backend import CircularBufferBackend
 
 
 def get_half_filled_buffer():
diff --git a/vel/rl/buffers/tests/test_circular_vec_env_buffer_backend.py b/vel/rl/buffer/test/test_circular_vec_env_buffer_backend.py
similarity index 99%
rename from vel/rl/buffers/tests/test_circular_vec_env_buffer_backend.py
rename to vel/rl/buffer/test/test_circular_vec_env_buffer_backend.py
index 7b45a77e..53336980 100644
--- a/vel/rl/buffers/tests/test_circular_vec_env_buffer_backend.py
+++ b/vel/rl/buffer/test/test_circular_vec_env_buffer_backend.py
@@ -4,8 +4,8 @@
 import numpy.testing as nt
 import pytest
 
-from vel.exceptions import VelException
-from vel.rl.buffers.circular_replay_buffer import CircularVecEnvBufferBackend
+from vel.exception import VelException
+from vel.rl.buffer.circular_replay_buffer import CircularVecEnvBufferBackend
 
 
 def get_half_filled_buffer(frame_history=1):
diff --git a/vel/rl/buffers/tests/test_prioritized_circular_buffer_backend.py b/vel/rl/buffer/test/test_prioritized_circular_buffer_backend.py
similarity index 96%
rename from vel/rl/buffers/tests/test_prioritized_circular_buffer_backend.py
rename to vel/rl/buffer/test/test_prioritized_circular_buffer_backend.py
index d9907ebc..f603d65e 100644
--- a/vel/rl/buffers/tests/test_prioritized_circular_buffer_backend.py
+++ b/vel/rl/buffer/test/test_prioritized_circular_buffer_backend.py
@@ -5,8 +5,8 @@
 import numpy.testing as nt
 import pytest
 
-from vel.exceptions import VelException
-from vel.rl.buffers.backend.prioritized_buffer_backend import PrioritizedCircularBufferBackend
+from vel.exception import VelException
+from vel.rl.buffer.backend.prioritized_buffer_backend import PrioritizedCircularBufferBackend
 
 
 def get_halfempty_buffer_with_dones():
diff --git a/vel/rl/buffers/tests/test_prioritized_vec_buffer_backend.py b/vel/rl/buffer/test/test_prioritized_vec_buffer_backend.py
similarity index 98%
rename from vel/rl/buffers/tests/test_prioritized_vec_buffer_backend.py
rename to vel/rl/buffer/test/test_prioritized_vec_buffer_backend.py
index cd90ebe6..5fb21f3b 100644
--- a/vel/rl/buffers/tests/test_prioritized_vec_buffer_backend.py
+++ b/vel/rl/buffer/test/test_prioritized_vec_buffer_backend.py
@@ -5,8 +5,8 @@
 import numpy.testing as nt
 import pytest
 
-from vel.exceptions import VelException
-from vel.rl.buffers.backend.prioritized_vec_buffer_backend import PrioritizedCircularVecEnvBufferBackend
+from vel.exception import VelException
+from vel.rl.buffer.backend.prioritized_vec_buffer_backend import PrioritizedCircularVecEnvBufferBackend
 
 
 def get_halfempty_buffer_with_dones(frame_history=1):
diff --git a/vel/sources/nlp/__init__.py b/vel/rl/command/__init__.py
similarity index 100%
rename from vel/sources/nlp/__init__.py
rename to vel/rl/command/__init__.py
diff --git a/vel/rl/commands/enjoy.py b/vel/rl/command/enjoy.py
similarity index 89%
rename from vel/rl/commands/enjoy.py
rename to vel/rl/command/enjoy.py
index c0a37508..2cef2bf2 100644
--- a/vel/rl/commands/enjoy.py
+++ b/vel/rl/command/enjoy.py
@@ -3,14 +3,14 @@
 import typing
 import time
 
-from vel.api import ModelConfig, TrainingInfo, Storage, ModelFactory
+from vel.api import ModelConfig, TrainingInfo, Storage, ModuleFactory
 from vel.rl.api import VecEnvFactory
 
 
 class EnjoyCommand:
     """ Play render("human") in a loop for a human to enjoy """
 
-    def __init__(self, model_config: ModelConfig, model_factory: ModelFactory, vec_env_factory: VecEnvFactory,
+    def __init__(self, model_config: ModelConfig, model_factory: ModuleFactory, vec_env_factory: VecEnvFactory,
                  storage: Storage, fps: float, sample_args: typing.Optional[dict]):
         self.model_config = model_config
         self.model_factory = model_factory
@@ -29,8 +29,7 @@ def run(self):
         model = self.model_factory.instantiate(action_space=env.action_space).to(device)
 
         training_info = TrainingInfo(
-            start_epoch_idx=self.storage.last_epoch_idx(),
-            run_name=self.model_config.run_name
+            start_epoch_idx=self.storage.last_epoch_idx()
         )
 
         self.storage.load(training_info, model)
@@ -46,14 +45,14 @@ def run_model(self, model, environment, device):
 
         seconds_per_frame = 1.0 / self.fps
 
-        if model.is_recurrent:
+        if model.is_stateful:
             hidden_state = model.zero_state(1).to(device)
 
         while True:
             observation_array = np.expand_dims(np.array(observation), axis=0)
             observation_tensor = torch.from_numpy(observation_array).to(device)
 
-            if model.is_recurrent:
+            if model.is_stateful:
                 output = model.step(observation_tensor, hidden_state, **self.sample_args)
                 hidden_state = output['state']
                 actions = output['actions']
diff --git a/vel/rl/commands/evaluate_env_command.py b/vel/rl/command/evaluate_env_command.py
similarity index 54%
rename from vel/rl/commands/evaluate_env_command.py
rename to vel/rl/command/evaluate_env_command.py
index eb5ef763..9596e109 100644
--- a/vel/rl/commands/evaluate_env_command.py
+++ b/vel/rl/command/evaluate_env_command.py
@@ -2,23 +2,22 @@
 import pandas as pd
 import torch
 import tqdm
-import typing
 
-from vel.api import ModelConfig, TrainingInfo, Storage, ModelFactory
+from vel.api import ModelConfig, TrainingInfo, Storage, ModuleFactory
 from vel.rl.api import VecEnvFactory
+from vel.rl.util.actor import PolicyActor
 
 
 class EvaluateEnvCommand:
     """ Record environment playthrough as a game  """
-    def __init__(self, model_config: ModelConfig, env_factory: VecEnvFactory, model_factory: ModelFactory,
-                 storage: Storage, parallel_envs: int, action_noise: typing.Optional[ModelFactory],  takes: int, sample_args: dict = None):
+    def __init__(self, model_config: ModelConfig, env_factory: VecEnvFactory, model_factory: ModuleFactory,
+                 storage: Storage, parallel_envs: int, takes: int, sample_args: dict = None):
         self.model_config = model_config
         self.model_factory = model_factory
         self.env_factory = env_factory
         self.storage = storage
         self.takes = takes
         self.parallel_envs = parallel_envs
-        self.action_noise_factory = action_noise
 
         self.sample_args = sample_args if sample_args is not None else {}
 
@@ -26,16 +25,15 @@ def __init__(self, model_config: ModelConfig, env_factory: VecEnvFactory, model_
     def run(self):
         device = self.model_config.torch_device()
 
-        env = self.env_factory.instantiate(parallel_envs=self.parallel_envs, preset='record', seed=self.model_config.seed)
-        model = self.model_factory.instantiate(action_space=env.action_space).to(device)
-
-        if self.action_noise_factory is not None:
-            action_noise = self.action_noise_factory.instantiate(environment=env).to(device)
-        else:
-            action_noise = None
+        env = self.env_factory.instantiate(
+            parallel_envs=self.parallel_envs, preset='record', seed=self.model_config.seed
+        )
+        model = self.model_factory.instantiate(
+            action_space=env.action_space, observation_space=env.observation_space
+        ).to(device)
 
         training_info = TrainingInfo(
-            start_epoch_idx=self.storage.last_epoch_idx(), run_name=self.model_config.run_name
+            start_epoch_idx=self.storage.last_epoch_idx()
         )
 
         model_state, hidden_state = self.storage.load(training_info)
@@ -45,26 +43,17 @@ def run(self):
 
         model.eval()
 
+        actor = PolicyActor(num_envs=self.parallel_envs, policy=model, device=device)
+
         episode_rewards = []
         episode_lengths = []
 
         observations = env.reset()
         observations_tensor = torch.from_numpy(observations).to(device)
 
-        if model.is_recurrent:
-            hidden_state = model.zero_state(observations.shape[0]).to(device)
-
         with tqdm.tqdm(total=self.takes) as progress_bar:
             while len(episode_rewards) < self.takes:
-                if model.is_recurrent:
-                    output = model.step(observations_tensor, hidden_state, **self.sample_args)
-                    hidden_state = output['state']
-                    actions = output['actions']
-                else:
-                    actions = model.step(observations_tensor, **self.sample_args)['actions']
-
-                if action_noise is not None:
-                    actions = action_noise(actions)
+                actions = actor.act(observations_tensor, **self.sample_args)['actions']
 
                 observations, rewards, dones, infos = env.step(actions.cpu().numpy())
                 observations_tensor = torch.from_numpy(observations).to(device)
@@ -75,22 +64,19 @@ def run(self):
                         episode_lengths.append(info['episode']['l'])
                         progress_bar.update(1)
 
-                if model.is_recurrent:
-                    # Zero state belongiong to finished episodes
-                    dones_tensor = torch.from_numpy(dones.astype(np.float32)).to(device)
-                    hidden_state = hidden_state * (1.0 - dones_tensor.unsqueeze(-1))
+                dones_tensor = torch.from_numpy(dones.astype(np.float32)).to(device)
+                actor.reset_states(dones_tensor)
 
         print(pd.DataFrame({'lengths': episode_lengths, 'rewards': episode_rewards}).describe())
 
 
-def create(model_config, model, vec_env, storage, takes, parallel_envs, action_noise=None, sample_args=None):
+def create(model_config, model, vec_env, storage, takes, parallel_envs, sample_args=None):
     """ Vel factory function """
     return EvaluateEnvCommand(
         model_config=model_config,
         model_factory=model,
         env_factory=vec_env,
         parallel_envs=parallel_envs,
-        action_noise=action_noise,
         storage=storage,
         takes=takes,
         sample_args=sample_args
diff --git a/vel/rl/commands/record_movie_command.py b/vel/rl/command/record_movie_command.py
similarity index 82%
rename from vel/rl/commands/record_movie_command.py
rename to vel/rl/command/record_movie_command.py
index b5c1ca75..12eee428 100644
--- a/vel/rl/commands/record_movie_command.py
+++ b/vel/rl/command/record_movie_command.py
@@ -7,13 +7,13 @@
 import tqdm
 import typing
 
-from vel.api import ModelConfig, TrainingInfo, Storage, ModelFactory
+from vel.api import ModelConfig, TrainingInfo, Storage, ModuleFactory
 from vel.rl.api import VecEnvFactory
 
 
 class RecordMovieCommand:
     """ Record environment playthrough as a game  """
-    def __init__(self, model_config: ModelConfig, env_factory: VecEnvFactory, model_factory: ModelFactory,
+    def __init__(self, model_config: ModelConfig, env_factory: VecEnvFactory, model_factory: ModuleFactory,
                  storage: Storage, videoname: str, takes: int, fps: int, sample_args: typing.Optional[dict] = None):
         self.model_config = model_config
         self.model_factory = model_factory
@@ -28,11 +28,10 @@ def run(self):
         device = self.model_config.torch_device()
 
         env = self.env_factory.instantiate_single(preset='record', seed=self.model_config.seed)
-        model = self.model_factory.instantiate(action_space=env.action_space).to(device)
+        model = self.model_factory.instantiate(action_space=env.action_space, observation_space=env.observation_space).to(device)
 
         training_info = TrainingInfo(
-            start_epoch_idx=self.storage.last_epoch_idx(),
-            run_name=self.model_config.run_name
+            start_epoch_idx=self.storage.last_epoch_idx()
         )
 
         model_state, hidden_state = self.storage.load(training_info)
@@ -50,7 +49,7 @@ def record_take(self, model, env_instance, device, take_number):
 
         observation = env_instance.reset()
 
-        if model.is_recurrent:
+        if model.is_stateful:
             hidden_state = model.zero_state(1).to(device)
 
         frames.append(env_instance.render('rgb_array'))
@@ -61,12 +60,12 @@ def record_take(self, model, env_instance, device, take_number):
             observation_array = np.expand_dims(np.array(observation), axis=0)
             observation_tensor = torch.from_numpy(observation_array).to(device)
 
-            if model.is_recurrent:
-                output = model.step(observation_tensor, hidden_state, **self.sample_args)
+            if model.is_stateful:
+                output = model.act(observation_tensor, hidden_state, **self.sample_args)
                 hidden_state = output['state']
                 actions = output['actions']
             else:
-                actions = model.step(observation_tensor, **self.sample_args)['actions']
+                actions = model.act(observation_tensor, **self.sample_args)['actions']
 
             actions = actions.detach().cpu().numpy()
 
@@ -78,7 +77,9 @@ def record_take(self, model, env_instance, device, take_number):
                 # End of an episode
                 break
 
-        takename = self.model_config.output_dir('videos', self.model_config.run_name, self.videoname.format(take_number))
+        takename = self.model_config.output_dir(
+            'videos', self.model_config.run_name, self.videoname.format(take_number)
+        )
         pathlib.Path(os.path.dirname(takename)).mkdir(parents=True, exist_ok=True)
 
         fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
diff --git a/vel/rl/command/rl_summary_command.py b/vel/rl/command/rl_summary_command.py
new file mode 100644
index 00000000..108a038b
--- /dev/null
+++ b/vel/rl/command/rl_summary_command.py
@@ -0,0 +1,16 @@
+class ModelSummary:
+    """ Just print model summary """
+    def __init__(self, model, vec_env):
+        self.model_factory = model
+        self.vec_env = vec_env
+
+    def run(self, *args):
+        """ Print model summary """
+        env = self.vec_env.instantiate(parallel_envs=1, seed=1)
+        model = self.model_factory.instantiate(action_space=env.action_space, observation_space=env.observation_space)
+        model.summary()
+
+
+def create(model, vec_env):
+    """ Vel factory function """
+    return ModelSummary(model, vec_env)
diff --git a/vel/rl/commands/rl_train_command.py b/vel/rl/command/rl_train_command.py
similarity index 78%
rename from vel/rl/commands/rl_train_command.py
rename to vel/rl/command/rl_train_command.py
index e1c0d9fb..6d6f0303 100644
--- a/vel/rl/commands/rl_train_command.py
+++ b/vel/rl/command/rl_train_command.py
@@ -1,8 +1,8 @@
 import typing
 
-from vel.api import ModelConfig, EpochInfo, TrainingInfo, BatchInfo, OptimizerFactory, Storage, Callback
-from vel.rl.api import ReinforcerFactory
-from vel.callbacks.time_tracker import TimeTracker
+from vel.api import ModelConfig, EpochInfo, TrainingInfo, BatchInfo, OptimizerFactory, Storage, Callback, VelOptimizer
+from vel.callback.time_tracker import TimeTracker
+from vel.rl.api import ReinforcerFactory, Reinforcer
 
 import vel.openai.baselines.logger as openai_logger
 
@@ -18,14 +18,14 @@ def on_initialization(self, training_info: TrainingInfo):
 
         training_info['frames'] = 0
 
-    def on_batch_begin(self, batch_info: BatchInfo):
+    def on_batch_begin(self, batch_info: BatchInfo, dataset: typing.Optional[str] = None):
         if 'total_frames' in batch_info.training_info:
             # Track progress during learning
             batch_info['progress'] = (
                 float(batch_info.training_info['frames']) / batch_info.training_info['total_frames']
             )
 
-    def on_batch_end(self, batch_info: BatchInfo):
+    def on_batch_end(self, batch_info: BatchInfo, dataset: typing.Optional[str] = None):
         batch_info.training_info['frames'] += batch_info['frames']
 
     def write_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict):
@@ -65,20 +65,12 @@ def run(self):
 
         # Reinforcer is the learner for the reinforcement learning model
         reinforcer = self.reinforcer.instantiate(device)
-        optimizer = self.optimizer_factory.instantiate(reinforcer.model)
+        optimizer = reinforcer.policy.create_optimizer(self.optimizer_factory)
 
-        # All callbacks used for learning
-        callbacks = self.gather_callbacks(optimizer)
-        # Metrics to track through this training
-        metrics = reinforcer.metrics()
-
-        training_info = self.resume_training(reinforcer, callbacks, metrics)
+        training_info = self.start_training(reinforcer, optimizer)
 
         reinforcer.initialize_training(training_info)
-        training_info.on_train_begin()
-
-        if training_info.optimizer_initial_state:
-            optimizer.load_state_dict(training_info.optimizer_initial_state)
+        training_info.on_train_begin(reinforcer.policy)
 
         global_epoch_idx = training_info.start_epoch_idx + 1
 
@@ -95,7 +87,7 @@ def run(self):
             if self.openai_logging:
                 self._openai_logging(epoch_info.result)
 
-            self.storage.checkpoint(epoch_info, reinforcer.model)
+            self.storage.checkpoint(epoch_info, reinforcer.policy)
 
             global_epoch_idx += 1
 
@@ -103,49 +95,57 @@ def run(self):
 
         return training_info
 
-    def gather_callbacks(self, optimizer) -> list:
-        """ Gather all the callbacks to be used in this training run """
+    def start_training(self, reinforcer: Reinforcer, optimizer: VelOptimizer) -> TrainingInfo:
+        """ Possibly resume training from a saved state from the storage """
+
+        if self.model_config.resume_training:
+            start_epoch = self.storage.last_epoch_idx()
+        else:
+            start_epoch = 0
+
         callbacks = [FrameTracker(self.total_frames), TimeTracker()]
 
         if self.scheduler_factory is not None:
-            callbacks.append(self.scheduler_factory.instantiate(optimizer))
+            callbacks.extend(
+                optimizer.create_scheduler(scheduler_factory=self.scheduler_factory, last_epoch=start_epoch-1)
+            )
 
         callbacks.extend(self.callbacks)
         callbacks.extend(self.storage.streaming_callbacks())
 
-        return callbacks
-
-    def resume_training(self, reinforcer, callbacks, metrics) -> TrainingInfo:
-        """ Possibly resume training from a saved state from the storage """
-        if self.model_config.continue_training:
-            start_epoch = self.storage.last_epoch_idx()
-        else:
-            start_epoch = 0
+        # Metrics to track through this training
+        metrics = reinforcer.metrics() + optimizer.metrics()
 
         training_info = TrainingInfo(
             start_epoch_idx=start_epoch,
-            run_name=self.model_config.run_name,
-            metrics=metrics, callbacks=callbacks
+            metrics=metrics,
+            callbacks=callbacks
         )
 
         if start_epoch == 0:
+            self.model_config.write_meta()
             self.storage.reset(self.model_config.render_configuration())
             training_info.initialize()
             reinforcer.initialize_training(training_info)
         else:
             model_state, hidden_state = self.storage.load(training_info)
+
+            training_info.restore(hidden_state)
             reinforcer.initialize_training(training_info, model_state, hidden_state)
 
+            if 'optimizer' in hidden_state:
+                optimizer.load_state_dict(hidden_state['optimizer'])
+
         return training_info
 
     def _openai_logging(self, epoch_result):
         """ Use OpenAI logging facilities for the same type of logging """
         for key in sorted(epoch_result.keys()):
-            if key == 'fps':
+            if key.name == 'fps':
                 # Not super elegant, but I like nicer display of FPS
-                openai_logger.record_tabular(key, int(epoch_result[key]))
+                openai_logger.record_tabular(key.name, int(epoch_result[key]))
             else:
-                openai_logger.record_tabular(key, epoch_result[key])
+                openai_logger.record_tabular(key.name, epoch_result[key])
 
         openai_logger.dump_tabular()
 
diff --git a/vel/rl/env/mujoco.py b/vel/rl/env/mujoco.py
index 3c511c69..9b16b8a0 100644
--- a/vel/rl/env/mujoco.py
+++ b/vel/rl/env/mujoco.py
@@ -6,28 +6,22 @@
 from vel.openai.baselines import logger
 from vel.openai.baselines.bench import Monitor
 from vel.rl.api import EnvFactory
-from vel.rl.env.wrappers.env_normalize import EnvNormalize
 from vel.util.situational import process_environment_settings
 
 
 DEFAULT_SETTINGS = {
     'default': {
         'monitor': False,
-        'allow_early_resets': False,
-        'normalize_observations': False,
-        'normalize_returns': False,
+        'allow_early_resets': False
     },
     'record': {
         'monitor': False,
-        'allow_early_resets': True,
-        'normalize_observations': False,
-        'normalize_returns': False,
+        'allow_early_resets': True
     }
 }
 
 
-def env_maker(environment_id, seed, serial_id, monitor=False, allow_early_resets=False, normalize_observations=False,
-              normalize_returns=False, normalize_gamma=0.99):
+def env_maker(environment_id, seed, serial_id, monitor=False, allow_early_resets=False):
     """ Create a relatively raw atari environment """
     env = gym.make(environment_id)
     env.seed(seed + serial_id)
@@ -40,30 +34,16 @@ def env_maker(environment_id, seed, serial_id, monitor=False, allow_early_resets
 
     env = Monitor(env, logdir, allow_early_resets=allow_early_resets)
 
-    if normalize_observations or normalize_returns:
-        env = EnvNormalize(
-            env,
-            normalize_observations=normalize_observations,
-            normalize_returns=normalize_returns,
-            gamma=normalize_gamma
-        )
-
     return env
 
 
 class MujocoEnv(EnvFactory):
     """ Atari game environment wrapped in the same way as Deep Mind and OpenAI baselines """
-    def __init__(self, envname, normalize_observations=False, normalize_returns=False, settings=None, presets=None):
+    def __init__(self, envname, settings=None, presets=None):
         self.envname = envname
 
         settings = settings if settings is not None else {}
 
-        if normalize_observations:
-            settings['normalize_observations'] = True
-
-        if normalize_returns:
-            settings['normalize_returns'] = True
-
         self.settings = process_environment_settings(DEFAULT_SETTINGS, settings, presets)
 
     def specification(self) -> EnvSpec:
@@ -80,11 +60,10 @@ def instantiate(self, seed=0, serial_id=0, preset='default', extra_args=None) ->
         return env_maker(self.envname, seed, serial_id, **settings)
 
 
-def create(game, normalize_returns=False, settings=None, presets=None):
+def create(game, settings=None, presets=None):
     """ Vel factory function """
     return MujocoEnv(
         envname=game,
-        normalize_returns=normalize_returns,
         settings=settings,
         presets=presets
     )
diff --git a/vel/rl/env/wrappers/env_normalize.py b/vel/rl/env/wrappers/env_normalize.py
deleted file mode 100644
index be21772f..00000000
--- a/vel/rl/env/wrappers/env_normalize.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import gym
-import numpy as np
-
-from vel.openai.baselines.common.running_mean_std import RunningMeanStd
-
-
-class EnvNormalize(gym.Wrapper):
-    """
-    Single environment normalization based on VecNormalize from OpenAI baselines
-    """
-    def __init__(self, env, normalize_observations=True, normalize_returns=True,
-                 clip_observations=10., clip_rewards=10., gamma=0.99, epsilon=1e-8):
-        super().__init__(env)
-
-        self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if normalize_observations else None
-        self.ret_rms = RunningMeanStd(shape=()) if normalize_returns else None
-        self.clipob = clip_observations
-        self.cliprew = clip_rewards
-        self.ret = 0.0
-        self.gamma = gamma
-        self.epsilon = epsilon
-
-    def step(self, action):
-        """
-        Apply sequence of actions to sequence of environments
-        actions -> (observations, rewards, news)
-
-        where 'news' is a boolean vector indicating whether each element is new.
-        """
-        obs, rews, news, infos = self.env.step(action)
-
-        self.ret = self.ret * self.gamma + rews
-
-        obs = self._filter_observation(obs)
-
-        if self.ret_rms:
-            self.ret_rms.update(np.array([self.ret]))
-            rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
-
-        return obs, rews, news, infos
-
-    def _filter_observation(self, obs):
-        if self.ob_rms:
-            self.ob_rms.update(obs[None])
-            obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)
-
-            return obs.astype(np.float32)
-        else:
-            return obs
-
-    def reset(self):
-        """
-        Reset all environments
-        """
-        obs = self.env.reset()
-        return self._filter_observation(obs)
diff --git a/vel/rl/env_roller/step_env_roller.py b/vel/rl/env_roller/step_env_roller.py
index b9af23bc..cd8b5cd1 100644
--- a/vel/rl/env_roller/step_env_roller.py
+++ b/vel/rl/env_roller/step_env_roller.py
@@ -1,9 +1,12 @@
 import torch
 import numpy as np
 
-from vel.api import BatchInfo, Model
-from vel.rl.api import Trajectories, Rollout, EnvRollerBase, EnvRollerFactoryBase
-from vel.util.tensor_accumulator import TensorAccumulator
+from vel.api import BatchInfo
+from vel.openai.baselines.common.vec_env import VecEnv
+from vel.rl.api import Trajectories, Rollout, EnvRollerBase, EnvRollerFactoryBase, RlPolicy
+from vel.rl.util.actor import PolicyActor
+from vel.util.tensor_util import TensorAccumulator, to_device
+from vel.util.datastructure import flatten_dict
 
 
 class StepEnvRoller(EnvRollerBase):
@@ -11,15 +14,14 @@ class StepEnvRoller(EnvRollerBase):
     Class calculating env rollouts.
     """
 
-    def __init__(self, environment, device):
+    def __init__(self, environment: VecEnv, policy: RlPolicy, device: torch.device):
         self._environment = environment
         self.device = device
 
         # Initial observation - kept on CPU
         self.last_observation = torch.from_numpy(self.environment.reset()).clone()
 
-        # Relevant for RNN policies - kept on DEVICE
-        self.hidden_state = None
+        self.actor = PolicyActor(self.environment.num_envs, policy, device)
 
     @property
     def environment(self):
@@ -27,64 +29,55 @@ def environment(self):
         return self._environment
 
     @torch.no_grad()
-    def rollout(self, batch_info: BatchInfo, model: Model, number_of_steps: int) -> Rollout:
+    def rollout(self, batch_info: BatchInfo, number_of_steps: int) -> Rollout:
         """ Calculate env rollout """
+        self.actor.eval()
         accumulator = TensorAccumulator()
         episode_information = []  # List of dictionaries with episode information
 
-        if self.hidden_state is None and model.is_recurrent:
-            self.hidden_state = model.zero_state(self.last_observation.size(0)).to(self.device)
-
-        # Remember rollout initial state, we'll use that for training as well
-        initial_hidden_state = self.hidden_state
-
         for step_idx in range(number_of_steps):
-            if model.is_recurrent:
-                step = model.step(self.last_observation.to(self.device), state=self.hidden_state)
-                self.hidden_state = step['state']
-            else:
-                step = model.step(self.last_observation.to(self.device))
+            step = self.actor.act(self.last_observation.to(self.device), deterministic=False)
+            cpu_step = to_device(step, torch.device('cpu'))
 
             # Add step to the tensor accumulator
-            for name, tensor in step.items():
-                accumulator.add(name, tensor.cpu())
+            for name, tensor in cpu_step.items():
+
+                # Take not that here we convert all the tensors to CPU
+                accumulator.add(name, tensor)
 
             accumulator.add('observations', self.last_observation)
 
-            actions_numpy = step['actions'].detach().cpu().numpy()
+            actions_numpy = cpu_step['actions'].detach().numpy()
             new_obs, new_rewards, new_dones, new_infos = self.environment.step(actions_numpy)
 
             # Done is flagged true when the episode has ended AND the frame we see is already a first frame from the
             # next episode
             dones_tensor = torch.from_numpy(new_dones.astype(np.float32)).clone()
-            accumulator.add('dones', dones_tensor)
 
             self.last_observation = torch.from_numpy(new_obs).clone()
+            self.actor.reset_states(dones_tensor)
 
-            if model.is_recurrent:
-                # Zero out state in environments that have finished
-                self.hidden_state = self.hidden_state * (1.0 - dones_tensor.unsqueeze(-1)).to(self.device)
-
+            accumulator.add('dones', dones_tensor)
             accumulator.add('rewards', torch.from_numpy(new_rewards.astype(np.float32)).clone())
 
             episode_information.append(new_infos)
 
-        if model.is_recurrent:
-            final_values = model.value(self.last_observation.to(self.device), state=self.hidden_state).cpu()
-        else:
-            final_values = model.value(self.last_observation.to(self.device)).cpu()
-
         accumulated_tensors = accumulator.result()
 
+        # Perform last agent step, without advancing the state
+        final_obs = self.actor.act(self.last_observation.to(self.device), advance_state=False)
+        cpu_final_obs = to_device(final_obs, torch.device('cpu'))
+
+        rollout_tensors = {}
+
+        flatten_dict(cpu_final_obs, rollout_tensors, root='final')
+
         return Trajectories(
             num_steps=accumulated_tensors['observations'].size(0),
             num_envs=accumulated_tensors['observations'].size(1),
             environment_information=episode_information,
             transition_tensors=accumulated_tensors,
-            rollout_tensors={
-                'initial_hidden_state': initial_hidden_state.cpu() if initial_hidden_state is not None else None,
-                'final_values': final_values
-            }
+            rollout_tensors=rollout_tensors
         )
 
 
@@ -93,9 +86,10 @@ class StepEnvRollerFactory(EnvRollerFactoryBase):
     def __init__(self):
         pass
 
-    def instantiate(self, environment, device):
+    def instantiate(self, environment, policy, device):
         return StepEnvRoller(
             environment=environment,
+            policy=policy,
             device=device,
         )
 
diff --git a/vel/rl/env_roller/trajectory_replay_env_roller.py b/vel/rl/env_roller/trajectory_replay_env_roller.py
index 87412ee1..be516637 100644
--- a/vel/rl/env_roller/trajectory_replay_env_roller.py
+++ b/vel/rl/env_roller/trajectory_replay_env_roller.py
@@ -2,10 +2,13 @@
 import numpy as np
 
 from vel.api import BatchInfo
+from vel.openai.baselines.common.vec_env import VecEnv
 from vel.rl.api import (
-    Trajectories, Rollout, ReplayEnvRollerBase, ReplayEnvRollerFactoryBase, ReplayBuffer, ReplayBufferFactory, RlModel
+    Trajectories, Rollout, ReplayEnvRollerBase, ReplayEnvRollerFactoryBase, ReplayBuffer, ReplayBufferFactory, RlPolicy
 )
-from vel.util.tensor_accumulator import TensorAccumulator
+from vel.rl.util.actor import PolicyActor
+from vel.util.tensor_util import TensorAccumulator, to_device
+from vel.util.datastructure import flatten_dict
 
 
 class TrajectoryReplayEnvRoller(ReplayEnvRollerBase):
@@ -15,11 +18,14 @@ class TrajectoryReplayEnvRoller(ReplayEnvRollerBase):
     Samples trajectories from the replay buffer (consecutive series of frames)
     """
 
-    def __init__(self, environment, device, replay_buffer: ReplayBuffer):
+    def __init__(self, environment: VecEnv, policy: RlPolicy, device: torch.device, replay_buffer: ReplayBuffer):
         self._environment = environment
         self.device = device
         self.replay_buffer = replay_buffer
 
+        self.actor = PolicyActor(self.environment.num_envs, policy, device)
+        assert not self.actor.is_stateful, "Does not support stateful policies"
+
         # Initial observation
         self.last_observation_cpu = torch.from_numpy(self.environment.reset()).clone()
         self.last_observation = self.last_observation_cpu.to(self.device)
@@ -30,15 +36,14 @@ def environment(self):
         return self._environment
 
     @torch.no_grad()
-    def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -> Rollout:
+    def rollout(self, batch_info: BatchInfo,  number_of_steps: int) -> Rollout:
         """ Calculate env rollout """
-        assert not model.is_recurrent, "Replay env roller does not support recurrent models"
-
+        self.actor.eval()
         accumulator = TensorAccumulator()
         episode_information = []  # List of dictionaries with episode information
 
         for step_idx in range(number_of_steps):
-            step = model.step(self.last_observation)
+            step = self.actor.act(self.last_observation, deterministic=False)
 
             replay_extra_information = {}
 
@@ -70,6 +75,8 @@ def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -
             dones_tensor = torch.from_numpy(new_dones.astype(np.float32)).clone()
             accumulator.add('dones', dones_tensor)
 
+            self.actor.reset_states(dones_tensor)
+
             self.last_observation_cpu = torch.from_numpy(new_obs).clone()
             self.last_observation = self.last_observation_cpu.to(self.device)
             accumulator.add('rewards', torch.from_numpy(new_rewards.astype(np.float32)).clone())
@@ -78,26 +85,31 @@ def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -
 
         accumulated_tensors = accumulator.result()
 
+        final_obs = self.actor.act(self.last_observation.to(self.device), advance_state=False)
+        cpu_final_obs = to_device(final_obs, torch.device('cpu'))
+
+        rollout_tensors = {}
+
+        flatten_dict(cpu_final_obs, rollout_tensors, root='final')
+
         return Trajectories(
             num_steps=accumulated_tensors['observations'].size(0),
             num_envs=accumulated_tensors['observations'].size(1),
             environment_information=episode_information,
             transition_tensors=accumulated_tensors,
-            rollout_tensors={
-                'final_values': model.value(self.last_observation).cpu()
-            }
+            rollout_tensors=rollout_tensors
         )
 
-    def sample(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -> Rollout:
+    def sample(self, batch_info: BatchInfo, number_of_steps: int) -> Rollout:
         """ Sample experience from replay buffer and return a batch """
         # Sample trajectories
         rollout = self.replay_buffer.sample_trajectories(rollout_length=number_of_steps, batch_info=batch_info)
 
         last_observations = rollout.transition_tensors['observations_next'][-1].to(self.device)
-        final_values = model.value(last_observations).cpu()
+        final_values = self.actor.value(last_observations).cpu()
 
         # Add 'final_values' to the rollout
-        rollout.rollout_tensors['final_values'] = final_values
+        rollout.rollout_tensors['final.values'] = final_values
 
         return rollout
 
@@ -116,11 +128,12 @@ class TrajectoryReplayEnvRollerFactory(ReplayEnvRollerFactoryBase):
     def __init__(self, replay_buffer_factory: ReplayBufferFactory):
         self.replay_buffer_factory = replay_buffer_factory
 
-    def instantiate(self, environment, device):
+    def instantiate(self, environment, policy, device):
         replay_buffer = self.replay_buffer_factory.instantiate(environment)
 
         return TrajectoryReplayEnvRoller(
             environment=environment,
+            policy=policy,
             device=device,
             replay_buffer=replay_buffer
         )
diff --git a/vel/rl/env_roller/transition_replay_env_roller.py b/vel/rl/env_roller/transition_replay_env_roller.py
index fc7e7fba..37c65c2f 100644
--- a/vel/rl/env_roller/transition_replay_env_roller.py
+++ b/vel/rl/env_roller/transition_replay_env_roller.py
@@ -1,14 +1,15 @@
 import torch
-import torch.nn as nn
 import typing
 import numpy as np
 
-from vel.api import BatchInfo, ModelFactory
+from vel.api import BatchInfo
+from vel.openai.baselines.common.vec_env import VecEnv
 from vel.openai.baselines.common.running_mean_std import RunningMeanStd
 from vel.rl.api import (
-    Trajectories, Rollout, ReplayEnvRollerBase, ReplayEnvRollerFactoryBase, RlModel, ReplayBuffer, ReplayBufferFactory
+    Trajectories, Rollout, ReplayEnvRollerBase, ReplayEnvRollerFactoryBase, ReplayBuffer, ReplayBufferFactory, RlPolicy
 )
-from vel.util.tensor_accumulator import TensorAccumulator
+from vel.rl.util.actor import PolicyActor
+from vel.util.tensor_util import TensorAccumulator
 
 
 class TransitionReplayEnvRoller(ReplayEnvRollerBase):
@@ -18,23 +19,16 @@ class TransitionReplayEnvRoller(ReplayEnvRollerBase):
     Samples transitions from the replay buffer (individual frame transitions)
     """
 
-    def __init__(self, environment, device, replay_buffer: ReplayBuffer, discount_factor: typing.Optional[float]=None,
-                 normalize_returns: bool=False, forward_steps: int=1, action_noise: typing.Optional[nn.Module]=None):
+    def __init__(self, environment: VecEnv, policy: RlPolicy, device: torch.device, replay_buffer: ReplayBuffer,
+                 normalize_returns: bool = False, forward_steps: int = 1):
         self._environment = environment
         self.device = device
         self.replay_buffer = replay_buffer
         self.normalize_returns = normalize_returns
         self.forward_steps = forward_steps
-        self.discount_factor = discount_factor
-        self.action_noise = action_noise.to(self.device) if action_noise is not None else None
 
-        if self.normalize_returns:
-            assert self.discount_factor is not None, \
-                "TransitionReplayEnvRoller must have a discount factor defined if normalize_returns is turned on"
-
-        if self.forward_steps > 1:
-            assert self.discount_factor is not None, \
-                "TransitionReplayEnvRoller must have a discount factor defined if forward_steps is larger than one"
+        self.actor = PolicyActor(self.environment.num_envs, policy, device)
+        assert not self.actor.is_stateful, "Does not support stateful policies"
 
         self.ret_rms = RunningMeanStd(shape=()) if normalize_returns else None
 
@@ -52,18 +46,15 @@ def environment(self):
         return self._environment
 
     @torch.no_grad()
-    def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -> Rollout:
+    def rollout(self, batch_info: BatchInfo, number_of_steps: int) -> Rollout:
         """ Calculate env rollout """
-        assert not model.is_recurrent, "Replay env roller does not support recurrent models"
+        self.actor.eval()
 
         accumulator = TensorAccumulator()
         episode_information = []  # List of dictionaries with episode information
 
         for step_idx in range(number_of_steps):
-            step = model.step(self.last_observation)
-
-            if self.action_noise is not None:
-                step['actions'] = self.action_noise(step['actions'], batch_info=batch_info)
+            step = self.actor.act(self.last_observation, deterministic=False)
 
             replay_extra_information = {}
 
@@ -90,7 +81,7 @@ def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -
             )
 
             if self.ret_rms is not None:
-                self.accumulated_returns = new_rewards + self.discount_factor * self.accumulated_returns
+                self.accumulated_returns = new_rewards + self.actor.discount_factor * self.accumulated_returns
                 self.ret_rms.update(self.accumulated_returns)
 
             # Done is flagged true when the episode has ended AND the frame we see is already a first frame from the
@@ -98,8 +89,7 @@ def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -
             dones_tensor = torch.from_numpy(new_dones.astype(np.float32)).clone()
             accumulator.add('dones', dones_tensor)
 
-            if self.action_noise is not None:
-                self.action_noise.reset_training_state(dones_tensor, batch_info=batch_info)
+            self.actor.reset_states(dones_tensor)
 
             self.accumulated_returns = self.accumulated_returns * (1.0 - new_dones.astype(np.float32))
 
@@ -123,12 +113,12 @@ def rollout(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -
             rollout_tensors={}
         ).to_transitions()
 
-    def sample(self, batch_info: BatchInfo, model: RlModel, number_of_steps: int) -> Rollout:
+    def sample(self, batch_info: BatchInfo, number_of_steps: int) -> Rollout:
         """ Sample experience from replay buffer and return a batch """
         if self.forward_steps > 1:
             transitions = self.replay_buffer.sample_forward_transitions(
                 batch_size=number_of_steps, batch_info=batch_info, forward_steps=self.forward_steps,
-                discount_factor=self.discount_factor
+                discount_factor=self.actor.discount_factor
             )
         else:
             transitions = self.replay_buffer.sample_transitions(batch_size=number_of_steps, batch_info=batch_info)
@@ -156,40 +146,29 @@ def update(self, rollout, batch_info):
 class TransitionReplayEnvRollerFactory(ReplayEnvRollerFactoryBase):
     """ Factory for the ReplayEnvRoller """
 
-    def __init__(self, replay_buffer_factory: ReplayBufferFactory, discount_factor: typing.Optional[float]=None,
-                 normalize_returns: bool=False, forward_steps: int=1, action_noise: typing.Optional[ModelFactory]=None):
+    def __init__(self, replay_buffer_factory: ReplayBufferFactory, normalize_returns: bool = False,
+                 forward_steps: int = 1):
         self.replay_buffer_factory = replay_buffer_factory
         self.normalize_returns = normalize_returns
         self.forward_steps = forward_steps
-        self.discount_factor = discount_factor
-        self.action_noise_factory = action_noise
 
-    def instantiate(self, environment, device):
+    def instantiate(self, environment, policy, device):
         replay_buffer = self.replay_buffer_factory.instantiate(environment)
 
-        if self.action_noise_factory is None:
-            action_noise = None
-        else:
-            action_noise = self.action_noise_factory.instantiate(environment=environment)
-
         return TransitionReplayEnvRoller(
             environment=environment,
+            policy=policy,
             device=device,
             replay_buffer=replay_buffer,
-            discount_factor=self.discount_factor,
             normalize_returns=self.normalize_returns,
-            forward_steps=self.forward_steps,
-            action_noise=action_noise
+            forward_steps=self.forward_steps
         )
 
 
-def create(replay_buffer, discount_factor: typing.Optional[float]=None, normalize_returns: bool=False,
-           forward_steps: int=1, action_noise: typing.Optional[ModelFactory]=None):
+def create(replay_buffer, normalize_returns: bool = False, forward_steps: int = 1):
     """ Vel factory function """
     return TransitionReplayEnvRollerFactory(
         replay_buffer_factory=replay_buffer,
-        discount_factor=discount_factor,
         forward_steps=forward_steps,
-        normalize_returns=normalize_returns,
-        action_noise=action_noise
+        normalize_returns=normalize_returns
     )
diff --git a/vel/sources/vision/__init__.py b/vel/rl/layer/__init__.py
similarity index 100%
rename from vel/sources/vision/__init__.py
rename to vel/rl/layer/__init__.py
diff --git a/vel/rl/models/backbone/double_nature_cnn.py b/vel/rl/layer/double_nature_cnn.py
similarity index 64%
rename from vel/rl/models/backbone/double_nature_cnn.py
rename to vel/rl/layer/double_nature_cnn.py
index ed64afcd..f053c3b2 100644
--- a/vel/rl/models/backbone/double_nature_cnn.py
+++ b/vel/rl/layer/double_nature_cnn.py
@@ -12,18 +12,19 @@
 
 import vel.util.network as net_util
 
-from vel.api import LinearBackboneModel, ModelFactory
+from vel.api import SizeHints, SizeHint
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
 
 
-class DoubleNatureCnn(LinearBackboneModel):
+class DoubleNatureCnn(Layer):
     """
     Neural network as defined in the paper 'Human-level control through deep reinforcement learning'
     but with two separate heads.
     """
-    def __init__(self, input_width, input_height, input_channels, output_dim=512):
-        super().__init__()
+    def __init__(self, info: LayerInfo, input_width, input_height, input_channels, output_dim=512):
+        super().__init__(info)
 
-        self._output_dim = output_dim
+        self.output_dim = output_dim
 
         self.conv1 = nn.Conv2d(
             in_channels=input_channels,
@@ -68,11 +69,6 @@ def __init__(self, input_width, input_height, input_channels, output_dim=512):
             self.output_dim
         )
 
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self._output_dim
-
     def reset_weights(self):
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
@@ -84,7 +80,13 @@ def reset_weights(self):
                 init.orthogonal_(m.weight, gain=np.sqrt(2))
                 init.constant_(m.bias, 0.0)
 
-    def forward(self, image):
+    def size_hints(self) -> SizeHints:
+        return SizeHints((
+            SizeHint(None, self.output_dim),
+            SizeHint(None, self.output_dim)
+        ))
+
+    def forward(self, image, state: dict = None, context: dict = None):
         result = image
         result = F.relu(self.conv1(result))
         result = F.relu(self.conv2(result))
@@ -97,12 +99,30 @@ def forward(self, image):
         return output_one, output_two
 
 
-def create(input_width, input_height, input_channels=1):
-    """ Vel factory function """
-    def instantiate(**_):
-        return DoubleNatureCnn(input_width=input_width, input_height=input_height, input_channels=input_channels)
+class DoubleNatureCnnFactory(LayerFactory):
+    """ Nature Cnn Network Factory """
 
-    return ModelFactory.generic(instantiate)
+    def __init__(self, output_dim: int = 512):
+        super().__init__()
+        self.output_dim = output_dim
 
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "double_nature_cnn"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        (b, c, w, h) = direct_input.assert_single(4)
+
+        return DoubleNatureCnn(
+            info=self.make_info(context),
+            input_width=w,
+            input_height=h,
+            input_channels=c,
+            output_dim=self.output_dim
+        )
 
-DoubleNatureCnnFactory = create
+
+def create(output_dim: int = 512, label=None, group=None):
+    """ Vel factory function """
+    return DoubleNatureCnnFactory(output_dim=output_dim).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/models/backbone/double_noisy_nature_cnn.py b/vel/rl/layer/double_noisy_nature_cnn.py
similarity index 56%
rename from vel/rl/models/backbone/double_noisy_nature_cnn.py
rename to vel/rl/layer/double_noisy_nature_cnn.py
index ca6626b5..da867a76 100644
--- a/vel/rl/models/backbone/double_noisy_nature_cnn.py
+++ b/vel/rl/layer/double_noisy_nature_cnn.py
@@ -12,20 +12,21 @@
 
 import vel.util.network as net_util
 
-from vel.api import LinearBackboneModel, ModelFactory
-from vel.rl.modules.noisy_linear import NoisyLinear
+from vel.api import SizeHints, SizeHint
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
+from vel.rl.module.noisy_linear import NoisyLinear
 
 
-class DoubleNoisyNatureCnn(LinearBackboneModel):
+class DoubleNoisyNatureCnn(Layer):
     """
     Neural network as defined in the paper 'Human-level control through deep reinforcement learning'
     but with two separate heads and "noisy" linear layer.
     """
-    def __init__(self, input_width, input_height, input_channels, output_dim=512, initial_std_dev=0.4,
+    def __init__(self, info: LayerInfo, input_width, input_height, input_channels, output_dim=512, initial_std_dev=0.4,
                  factorized_noise=True):
-        super().__init__()
+        super().__init__(info)
 
-        self._output_dim = output_dim
+        self.output_dim = output_dim
 
         self.conv1 = nn.Conv2d(
             in_channels=input_channels,
@@ -76,10 +77,11 @@ def __init__(self, input_width, input_height, input_channels, output_dim=512, in
             factorized_noise=factorized_noise
         )
 
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self._output_dim
+    def size_hints(self) -> SizeHints:
+        return SizeHints((
+            SizeHint(None, self.output_dim),
+            SizeHint(None, self.output_dim)
+        ))
 
     def reset_weights(self):
         for m in self.modules():
@@ -94,28 +96,57 @@ def reset_weights(self):
             elif isinstance(m, NoisyLinear):
                 m.reset_weights()
 
-    def forward(self, image):
+    def forward(self, image, state: dict = None, context: dict = None):
+        if context is not None:
+            deterministic = context.get('deterministic', False)
+        else:
+            deterministic = False
+
         result = image
         result = F.relu(self.conv1(result))
         result = F.relu(self.conv2(result))
         result = F.relu(self.conv3(result))
         flattened = result.view(result.size(0), -1)
 
-        output_one = F.relu(self.linear_layer_one(flattened))
-        output_two = F.relu(self.linear_layer_two(flattened))
+        output_one = F.relu(self.linear_layer_one(flattened, deterministic=deterministic))
+        output_two = F.relu(self.linear_layer_two(flattened, deterministic=deterministic))
 
         return output_one, output_two
 
 
-def create(input_width, input_height, input_channels=1, output_dim=512, initial_std_dev=0.4, factorized_noise=True):
-    """ Vel factory function """
-    def instantiate(**_):
+class DoubleNoisyNatureCnnFactory(LayerFactory):
+    """ Nature Cnn Network Factory """
+
+    def __init__(self, initial_std_dev: float = 0.4, factorized_noise: bool = True, output_dim: int = 512):
+        super().__init__()
+        self.initial_std_dev = initial_std_dev
+        self.factorized_noise = factorized_noise
+        self.output_dim = output_dim
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "double_noisy_nature_cnn"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        (b, c, w, h) = direct_input.assert_single(4)
+
         return DoubleNoisyNatureCnn(
-            input_width=input_width, input_height=input_height, input_channels=input_channels,
-            output_dim=output_dim, initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
+            info=self.make_info(context),
+            input_width=w,
+            input_height=h,
+            input_channels=c,
+            output_dim=self.output_dim,
+            initial_std_dev=self.initial_std_dev,
+            factorized_noise=self.factorized_noise
         )
 
-    return ModelFactory.generic(instantiate)
-
 
-DoubleNoisyNatureCnnFactory = create
+def create(initial_std_dev: float = 0.4, factorized_noise: bool = True, output_dim: int = 512,
+           label=None, group=None):
+    """ Vel factory function """
+    return DoubleNoisyNatureCnnFactory(
+        output_dim=output_dim,
+        initial_std_dev=initial_std_dev,
+        factorized_noise=factorized_noise
+    ).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/layer/input/__init__.py b/vel/rl/layer/input/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/rl/models/backbone/nature_cnn.py b/vel/rl/layer/nature_cnn.py
similarity index 65%
rename from vel/rl/models/backbone/nature_cnn.py
rename to vel/rl/layer/nature_cnn.py
index 8ce20ce6..a30b60ce 100644
--- a/vel/rl/models/backbone/nature_cnn.py
+++ b/vel/rl/layer/nature_cnn.py
@@ -12,15 +12,17 @@
 
 import vel.util.network as net_util
 
-from vel.api import LinearBackboneModel, ModelFactory
+from vel.api import SizeHint, SizeHints
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
 
 
-class NatureCnn(LinearBackboneModel):
+class NatureCnn(Layer):
     """ Neural network as defined in the paper 'Human-level control through deep reinforcement learning' """
-    def __init__(self, input_width, input_height, input_channels, output_dim=512):
-        super().__init__()
 
-        self._output_dim = output_dim
+    def __init__(self, info: LayerInfo, input_width, input_height, input_channels, output_dim=512):
+        super().__init__(info)
+
+        self.output_dim = output_dim
 
         self.conv1 = nn.Conv2d(
             in_channels=input_channels,
@@ -57,11 +59,6 @@ def __init__(self, input_width, input_height, input_channels, output_dim=512):
             self.output_dim
         )
 
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self._output_dim
-
     def reset_weights(self):
         """ Call proper initializers for the weights """
         for m in self.modules():
@@ -74,7 +71,10 @@ def reset_weights(self):
                 init.orthogonal_(m.weight, gain=np.sqrt(2))
                 init.constant_(m.bias, 0.0)
 
-    def forward(self, image):
+    def size_hints(self) -> SizeHints:
+        return SizeHints(SizeHint(None, self.output_dim))
+
+    def forward(self, image, state: dict = None, context: dict = None):
         result = image
         result = F.relu(self.conv1(result))
         result = F.relu(self.conv2(result))
@@ -83,16 +83,30 @@ def forward(self, image):
         return F.relu(self.linear_layer(flattened))
 
 
-def create(input_width, input_height, input_channels=1, output_dim=512):
-    """ Vel factory function """
-    def instantiate(**_):
+class NatureCnnFactory(LayerFactory):
+    """ Nature Cnn Network Factory """
+
+    def __init__(self, output_dim: int = 512):
+        super().__init__()
+        self.output_dim = output_dim
+
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "nature_cnn"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        (b, c, w, h) = direct_input.assert_single(4)
+
         return NatureCnn(
-            input_width=input_width, input_height=input_height, input_channels=input_channels,
-            output_dim=output_dim
+            info=self.make_info(context),
+            input_width=w,
+            input_height=h,
+            input_channels=c,
+            output_dim=self.output_dim
         )
 
-    return ModelFactory.generic(instantiate)
-
 
-# Scripting interface
-NatureCnnFactory = create
+def create(output_dim=512, label=None, group=None):
+    """ Vel factory function """
+    return NatureCnnFactory(output_dim=output_dim).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/models/backbone/nature_cnn_small.py b/vel/rl/layer/nature_cnn_small.py
similarity index 60%
rename from vel/rl/models/backbone/nature_cnn_small.py
rename to vel/rl/layer/nature_cnn_small.py
index 29359262..ec8d9497 100644
--- a/vel/rl/models/backbone/nature_cnn_small.py
+++ b/vel/rl/layer/nature_cnn_small.py
@@ -12,18 +12,19 @@
 
 import vel.util.network as net_util
 
-from vel.api import LinearBackboneModel, ModelFactory
+from vel.api import SizeHint, SizeHints
+from vel.net.layer_base import LayerFactoryContext, Layer, LayerFactory, LayerInfo
 
 
-class NatureCnnSmall(LinearBackboneModel):
+class NatureCnnSmall(Layer):
     """
     Neural network as defined in the paper 'Human-level control through deep reinforcement learning'
     Smaller version.
     """
-    def __init__(self, input_width, input_height, input_channels, output_dim=128):
-        super().__init__()
+    def __init__(self, info: LayerInfo, input_width, input_height, input_channels, output_dim=128):
+        super().__init__(info)
 
-        self._output_dim = output_dim
+        self.output_dim = output_dim
 
         self.conv1 = nn.Conv2d(
             in_channels=input_channels,
@@ -54,11 +55,6 @@ def __init__(self, input_width, input_height, input_channels, output_dim=128):
             self.output_dim
         )
 
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self._output_dim
-
     def reset_weights(self):
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
@@ -70,7 +66,10 @@ def reset_weights(self):
                 init.orthogonal_(m.weight, gain=np.sqrt(2))
                 init.constant_(m.bias, 0.0)
 
-    def forward(self, image):
+    def size_hints(self) -> SizeHints:
+        return SizeHints(SizeHint(None, self.output_dim))
+
+    def forward(self, image, state: dict = None, context: dict = None):
         result = image
         result = F.relu(self.conv1(result))
         result = F.relu(self.conv2(result))
@@ -78,12 +77,30 @@ def forward(self, image):
         return F.relu(self.linear_layer(flattened))
 
 
-def create(input_width, input_height, input_channels=1):
-    """ Vel factory function """
-    def instantiate(**_):
-        return NatureCnnSmall(input_width=input_width, input_height=input_height, input_channels=input_channels)
+class NatureCnnSmallFactory(LayerFactory):
+    """ Nature Cnn Network Factory """
 
-    return ModelFactory.generic(instantiate)
+    def __init__(self, output_dim: int = 128):
+        super().__init__()
+        self.output_dim = output_dim
 
+    @property
+    def name_base(self) -> str:
+        """ Base of layer name """
+        return "nature_cnn_small"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        (b, c, w, h) = direct_input.assert_single(4)
+
+        return NatureCnnSmall(
+            info=self.make_info(context),
+            input_width=w,
+            input_height=h,
+            input_channels=c,
+            output_dim=self.output_dim
+        )
 
-NatureCnnSmallFactory = create
+
+def create(output_dim: int = 128, label=None, group=None):
+    """ Vel factory function """
+    return NatureCnnSmallFactory(output_dim=output_dim).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/layer/rnn_cell.py b/vel/rl/layer/rnn_cell.py
new file mode 100644
index 00000000..2eadf942
--- /dev/null
+++ b/vel/rl/layer/rnn_cell.py
@@ -0,0 +1,107 @@
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+
+
+from vel.api import SizeHint, SizeHints
+from vel.net.layer_base import Layer, LayerFactory, LayerFactoryContext, LayerInfo
+
+
+class RnnCell(Layer):
+    """ Generalization of RNN cell (Simple RNN, LSTM or GRU) """
+
+    def __init__(self, info: LayerInfo, input_size: int, hidden_size: int, rnn_type: str, bias: bool = True,
+                 nonlinearity: str = 'tanh'):
+        super().__init__(info)
+
+        assert rnn_type in {'rnn', 'lstm', 'gru'}, "Rnn type {} is not supported".format(rnn_type)
+
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.rnn_type = rnn_type
+
+        if self.rnn_type == 'rnn':
+            self.rnn_cell = nn.RNNCell(
+                input_size=input_size, hidden_size=hidden_size, bias=bias, nonlinearity=nonlinearity
+            )
+        elif self.rnn_type == 'lstm':
+            self.rnn_cell = nn.LSTMCell(input_size=input_size, hidden_size=hidden_size, bias=bias)
+        elif self.rnn_type == 'gru':
+            self.rnn_cell = nn.GRUCell(input_size=input_size, hidden_size=hidden_size, bias=bias)
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return True
+
+    def reset_weights(self):
+        init.xavier_normal_(self.rnn_cell.weight_hh)
+        init.xavier_normal_(self.rnn_cell.weight_ih)
+        init.zeros_(self.rnn_cell.bias_ih)
+        init.zeros_(self.rnn_cell.bias_hh)
+
+    def size_hints(self) -> SizeHints:
+        return SizeHints(SizeHint(None, self.hidden_size))
+
+    @property
+    def state_dim(self) -> int:
+        """ Dimension of model state """
+        if self.rnn_type == 'lstm':
+            return 2 * self.hidden_size
+        else:
+            return self.hidden_size
+
+    def zero_state(self, batch_size):
+        """ Potential state for the model """
+        return {self.name: torch.zeros(batch_size, self.state_dim)}
+
+    def forward(self, input_data, state: dict, context: dict = None):
+        """ Forward propagation of a single layer """
+        if self.rnn_type == 'lstm':
+            state_tensor = state[self.name]
+            hidden_state, cell_state = torch.split(state_tensor, self.hidden_size, 1)
+            hidden_state, cell_state = self.rnn_cell(input_data, (hidden_state, cell_state))
+            new_state = torch.cat([hidden_state, cell_state], dim=1)
+            return hidden_state, {self.name: new_state}
+        else:
+            state_tensor = state[self.name]
+            new_hidden_state = self.rnn_cell(input_data, state_tensor)
+            return new_hidden_state, {self.name: new_hidden_state}
+
+
+class RnnCellFactory(LayerFactory):
+    """ Factory for the RnnCell layer """
+
+    def __init__(self, hidden_size: int, rnn_type: str, bias: bool = True, nonlinearity: str = 'tanh'):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.rnn_type = rnn_type
+        self.bias = bias
+        self.nonlinearity = nonlinearity
+
+    @property
+    def name_base(self) -> str:
+        return "rnn_cell"
+
+    def instantiate(self, direct_input: SizeHints, context: LayerFactoryContext, extra_args: dict) -> Layer:
+        """ Create a given layer object """
+        input_size = direct_input.assert_single().last()
+
+        return RnnCell(
+            info=self.make_info(context),
+            input_size=input_size,
+            hidden_size=self.hidden_size,
+            rnn_type=self.rnn_type,
+            bias=self.bias,
+            nonlinearity=self.nonlinearity
+        )
+
+
+def create(hidden_size: int, rnn_type: str, bias: bool = True, nonlinearity: str = 'tanh', label=None, group=None):
+    """ Vel factory function """
+    return RnnCellFactory(
+        hidden_size=hidden_size,
+        rnn_type=rnn_type,
+        bias=bias,
+        nonlinearity=nonlinearity
+    ).with_given_name(label).with_given_group(group)
diff --git a/vel/rl/metrics.py b/vel/rl/metrics.py
index 3f5be7b2..d41cf25a 100644
--- a/vel/rl/metrics.py
+++ b/vel/rl/metrics.py
@@ -4,7 +4,7 @@
 import torch
 
 from vel.api import BatchInfo
-from vel.api.metrics import BaseMetric, AveragingMetric, ValueMetric
+from vel.metric import BaseMetric, AveragingMetric, ValueMetric
 
 
 class FramesMetric(ValueMetric):
diff --git a/vel/rl/models/backbone/lstm.py b/vel/rl/models/backbone/lstm.py
deleted file mode 100644
index eb2b90b4..00000000
--- a/vel/rl/models/backbone/lstm.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from vel.api import RnnLinearBackboneModel, ModelFactory
-
-
-class LstmBackbone(RnnLinearBackboneModel):
-    """
-    Simple 'LSTM' model backbone
-    """
-
-    def __init__(self, input_size, hidden_units):
-        super().__init__()
-
-        self.input_size = input_size
-        self.hidden_units = hidden_units
-
-    def forward(self, input_data, masks, state):
-        raise NotImplementedError
-
-    def initial_state(self):
-        """ Initial state of the network """
-        raise NotImplementedError
diff --git a/vel/rl/models/backbone/mlp.py b/vel/rl/models/backbone/mlp.py
deleted file mode 100644
index cc87e9e9..00000000
--- a/vel/rl/models/backbone/mlp.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""
-Code based loosely on the implementation:
-https://github.com/openai/baselines/blob/master/baselines/common/models.py
-
-Under MIT license.
-"""
-import typing
-import numpy as np
-
-import torch.nn as nn
-import torch.nn.init as init
-
-import vel.util.network as net_util
-
-from vel.api import LinearBackboneModel, ModelFactory
-
-
-class MLP(LinearBackboneModel):
-    """ Simple Multi-Layer-Perceptron network """
-    def __init__(self, input_length: int, hidden_layers: typing.List[int], activation: str='tanh',
-                 normalization: typing.Optional[str]=None):
-        super().__init__()
-
-        self.input_length = input_length
-        self.hidden_layers = hidden_layers
-        self.activation = activation
-        self.normalization = normalization
-
-        layer_objects = []
-        layer_sizes = zip([input_length] + hidden_layers, hidden_layers)
-
-        for input_size, output_size in layer_sizes:
-            layer_objects.append(nn.Linear(input_size, output_size))
-
-            if self.normalization:
-                layer_objects.append(net_util.normalization(normalization)(output_size))
-
-            layer_objects.append(net_util.activation(activation)())
-
-        self.model = nn.Sequential(*layer_objects)
-        self.hidden_units = hidden_layers[-1] if hidden_layers else input_length
-
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self.hidden_units
-
-    def reset_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Linear):
-                # init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                init.orthogonal_(m.weight, gain=np.sqrt(2))
-                init.constant_(m.bias, 0.0)
-
-    def forward(self, input_data):
-        input_data = input_data.float()
-        return self.model(input_data)
-
-
-def create(input_length, hidden_layers, activation='tanh', normalization=None):
-    """ Vel factory function """
-    def instantiate(**_):
-        return MLP(
-            input_length=input_length,
-            hidden_layers=hidden_layers,
-            activation=activation,
-            normalization=normalization
-        )
-
-    return ModelFactory.generic(instantiate)
-
-
-MLPFactory = create
diff --git a/vel/rl/models/backbone/nature_cnn_rnn.py b/vel/rl/models/backbone/nature_cnn_rnn.py
deleted file mode 100644
index 837e7a58..00000000
--- a/vel/rl/models/backbone/nature_cnn_rnn.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from vel.api import RnnLinearBackboneModel, ModelFactory
-from vel.rl.models.backbone.nature_cnn import NatureCnn
-from vel.modules.rnn_cell import RnnCell
-
-
-class NatureCnnRnnBackbone(RnnLinearBackboneModel):
-    """
-    Long-Short-Term Memory rnn cell together with DeepMind-style 'Nature' cnn preprocessing
-    """
-
-    def __init__(self, input_width: int, input_height: int, input_channels: int, rnn_type='lstm',
-                 cnn_output_dim: int=512, hidden_units: int=128):
-        super().__init__()
-
-        self.hidden_units = hidden_units
-
-        self.nature_cnn = NatureCnn(input_width, input_height, input_channels, cnn_output_dim)
-        self.rnn_cell = RnnCell(input_size=self.nature_cnn.output_dim, hidden_size=self.hidden_units, rnn_type=rnn_type)
-
-    def reset_weights(self):
-        """ Call proper initializers for the weights """
-        self.nature_cnn.reset_weights()
-        self.rnn_cell.reset_weights()
-
-    @property
-    def output_dim(self) -> int:
-        return self.rnn_cell.output_dim
-
-    @property
-    def state_dim(self) -> int:
-        """ Initial state of the network """
-        return self.rnn_cell.state_dim
-
-    def forward(self, input_image, state):
-        cnn_output = self.nature_cnn(input_image)
-        hidden_state, new_state = self.rnn_cell(cnn_output, state)
-
-        return hidden_state, new_state
-
-
-def create(input_width, input_height, input_channels=1, rnn_type='lstm', cnn_output_dim=512, hidden_units=128):
-    """ Vel factory function """
-    def instantiate(**_):
-        return NatureCnnRnnBackbone(
-            input_width=input_width, input_height=input_height, input_channels=input_channels,
-            rnn_type=rnn_type, cnn_output_dim=cnn_output_dim, hidden_units=hidden_units
-        )
-
-    return ModelFactory.generic(instantiate)
-
-
-# Add this to make nicer scripting interface
-NatureCnnFactory = create
diff --git a/vel/rl/models/backbone/noisy_nature_cnn.py b/vel/rl/models/backbone/noisy_nature_cnn.py
deleted file mode 100644
index 7f5e3b64..00000000
--- a/vel/rl/models/backbone/noisy_nature_cnn.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""
-Code based loosely on implementation:
-https://github.com/openai/baselines/blob/master/baselines/ppo2/policies.py
-
-Under MIT license.
-"""
-import numpy as np
-
-import torch.nn as nn
-import torch.nn.init as init
-import torch.nn.functional as F
-
-import vel.util.network as net_util
-
-from vel.api import LinearBackboneModel, ModelFactory
-from vel.rl.modules.noisy_linear import NoisyLinear
-
-
-class NoisyNatureCnn(LinearBackboneModel):
-    """
-    Neural network as defined in the paper 'Human-level control through deep reinforcement learning'
-    implemented via "Noisy Networks for Exploration"
-    """
-    def __init__(self, input_width, input_height, input_channels, output_dim=512, initial_std_dev=0.4,
-                 factorized_noise=True):
-        super().__init__()
-
-        self._output_dim = output_dim
-
-        self.conv1 = nn.Conv2d(
-            in_channels=input_channels,
-            out_channels=32,
-            kernel_size=(8, 8),
-            stride=4
-        )
-
-        self.conv2 = nn.Conv2d(
-            in_channels=32,
-            out_channels=64,
-            kernel_size=(4, 4),
-            stride=2
-        )
-
-        self.conv3 = nn.Conv2d(
-            in_channels=64,
-            out_channels=64,
-            kernel_size=(3, 3),
-            stride=1
-        )
-
-        layer_series = [
-            (8, 0, 4),
-            (4, 0, 2),
-            (3, 0, 1)
-        ]
-
-        self.final_width = net_util.convolutional_layer_series(input_width, layer_series)
-        self.final_height = net_util.convolutional_layer_series(input_height, layer_series)
-
-        self.linear_layer = NoisyLinear(
-            self.final_width * self.final_height * 64,  # 64 is the number of channels of the last conv layer
-            self.output_dim,
-            initial_std_dev=initial_std_dev,
-            factorized_noise=factorized_noise
-        )
-
-    @property
-    def output_dim(self) -> int:
-        """ Final dimension of model output """
-        return self._output_dim
-
-    def reset_weights(self):
-        """ Call proper initializers for the weights """
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                # init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                init.orthogonal_(m.weight, gain=np.sqrt(2))
-                init.constant_(m.bias, 0.0)
-            elif isinstance(m, nn.Linear):
-                # init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                init.orthogonal_(m.weight, gain=np.sqrt(2))
-                init.constant_(m.bias, 0.0)
-            elif isinstance(m, NoisyLinear):
-                m.reset_weights()
-
-    def forward(self, image):
-        result = image
-        result = F.relu(self.conv1(result))
-        result = F.relu(self.conv2(result))
-        result = F.relu(self.conv3(result))
-        flattened = result.view(result.size(0), -1)
-        return F.relu(self.linear_layer(flattened))
-
-
-def create(input_width, input_height, input_channels=1, output_dim=512, initial_std_dev=0.4, factorized_noise=True):
-    """ Vel factory function """
-    def instantiate(**_):
-        return NoisyNatureCnn(
-            input_width=input_width, input_height=input_height, input_channels=input_channels,
-            output_dim=output_dim, initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
-        )
-
-    return ModelFactory.generic(instantiate)
-
-
-# Scripting interface
-NatureCnnFactory = create
diff --git a/vel/rl/models/deterministic_policy_model.py b/vel/rl/models/deterministic_policy_model.py
deleted file mode 100644
index 6be633ce..00000000
--- a/vel/rl/models/deterministic_policy_model.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import gym
-import itertools as it
-import torch
-import typing
-
-from vel.api import LinearBackboneModel, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, Evaluator, RlModel
-from vel.rl.modules.deterministic_action_head import DeterministicActionHead
-from vel.rl.modules.deterministic_critic_head import DeterministicCriticHead
-
-
-class DeterministicPolicyEvaluator(Evaluator):
-    """ Evaluator for DeterministicPolicyModel """
-
-    def __init__(self, model: 'DeterministicPolicyModel', rollout: Rollout):
-        super().__init__(rollout)
-
-        self.model = model
-
-    @Evaluator.provides('model:values_next')
-    def model_estimated_values_next(self):
-        """ Estimate state-value of the transition next state """
-        observations = self.get('rollout:observations_next')
-        action, value = self.model(observations)
-        return value
-
-    @Evaluator.provides('model:actions')
-    def model_actions(self):
-        """ Estimate state-value of the transition next state """
-        observations = self.get('rollout:observations')
-        model_action = self.model.action(observations)
-        return model_action
-
-    @Evaluator.provides('model:model_action:q')
-    def model_model_action_q(self):
-        observations = self.get('rollout:observations')
-        model_actions = self.get('model:actions')
-        return self.model.value(observations, model_actions)
-
-    @Evaluator.provides('model:action:q')
-    def model_action_q(self):
-        observations = self.get('rollout:observations')
-        rollout_actions = self.get('rollout:actions')
-        return self.model.value(observations, rollout_actions)
-
-
-class DeterministicPolicyModel(RlModel):
-    """ Deterministic Policy Gradient - model """
-
-    def __init__(self, input_block: BackboneModel, policy_backbone: LinearBackboneModel,
-                 value_backbone: LinearBackboneModel, action_space: gym.Space):
-        super().__init__()
-
-        self.input_block = input_block
-        self.policy_backbone = policy_backbone
-        self.value_backbone = value_backbone
-
-        self.action_head = DeterministicActionHead(self.policy_backbone.output_dim, action_space)
-        self.critic_head = DeterministicCriticHead(self.value_backbone.output_dim)
-
-    def reset_weights(self):
-        """ Initialize properly model weights """
-        self.input_block.reset_weights()
-        self.policy_backbone.reset_weights()
-        self.value_backbone.reset_weights()
-        self.action_head.reset_weights()
-        self.critic_head.reset_weights()
-
-    def forward(self, observations, input_actions=None):
-        """ Calculate model outputs """
-        observations = self.input_block(observations)
-
-        if input_actions is not None:
-            actions = input_actions
-
-            value_input = torch.cat([observations, actions], dim=1)
-            value_hidden = self.value_backbone(value_input)
-
-            values = self.critic_head(value_hidden)
-        else:
-            policy_hidden = self.policy_backbone(observations)
-            actions = self.action_head(policy_hidden)
-
-            value_input = torch.cat([observations, actions], dim=1)
-            value_hidden = self.value_backbone(value_input)
-
-            values = self.critic_head(value_hidden)
-
-        return actions, values
-
-    def policy_parameters(self):
-        """ Parameters of policy """
-        return it.chain(self.policy_backbone.parameters(), self.action_head.parameters())
-
-    def value_parameters(self):
-        """ Parameters of policy """
-        return it.chain(self.value_backbone.parameters(), self.critic_head.parameters())
-
-    def get_layer_groups(self):
-        """ Return layers grouped """
-        return [
-            [self.policy_backbone, self.action_head],
-            [self.value_backbone, [y for (x, y) in self.critic_head.named_parameters() if x.endswith('bias')]],
-            # OpenAI regularizes only weight on the last layer. I'm just replicating that
-            [[y for (x, y) in self.critic_head.named_parameters() if x.endswith('weight')]]
-        ]
-
-    def step(self, observations):
-        """ Select actions based on model's output """
-        action, value = self(observations)
-
-        return {
-            'actions': action,
-            'values': value
-        }
-
-    def value(self, observation, input_actions=None):
-        """ Calculate value for given state """
-        action, value = self(observation, input_actions)
-        return value
-
-    def action(self, observations):
-        """ Calculate value for given state """
-        observations = self.input_block(observations)
-        policy_hidden = self.policy_backbone(observations)
-        action = self.action_head(policy_hidden)
-        return action
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return DeterministicPolicyEvaluator(self, rollout)
-
-
-class DeterministicPolicyModelFactory(ModelFactory):
-    """ Factory class for policy gradient models """
-    def __init__(self, input_block: ModelFactory, policy_backbone: ModelFactory, value_backbone: ModelFactory):
-        self.input_block = input_block
-        self.policy_backbone = policy_backbone
-        self.value_backbone = value_backbone
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        policy_backbone = self.policy_backbone.instantiate(**extra_args)
-        value_backbone = self.value_backbone.instantiate(**extra_args)
-
-        return DeterministicPolicyModel(
-            input_block=input_block,
-            policy_backbone=policy_backbone,
-            value_backbone=value_backbone,
-            action_space=extra_args['action_space'],
-        )
-
-
-def create(policy_backbone: ModelFactory, value_backbone: ModelFactory,
-           input_block: typing.Optional[ModelFactory]=None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return DeterministicPolicyModelFactory(
-        input_block=input_block, policy_backbone=policy_backbone, value_backbone=value_backbone
-    )
diff --git a/vel/rl/models/q_distributional_model.py b/vel/rl/models/q_distributional_model.py
deleted file mode 100644
index a769e741..00000000
--- a/vel/rl/models/q_distributional_model.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gym
-import typing
-
-from vel.api import LinearBackboneModel, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, RlModel, Evaluator
-from vel.rl.modules.q_distributional_head import QDistributionalHead
-
-
-class QDistributionalModelEvaluator(Evaluator):
-    """ Evaluate distributional q-model """
-    def __init__(self, model: 'QDistributionalModel', rollout: Rollout):
-        super().__init__(rollout)
-        self.model = model
-
-    @Evaluator.provides('model:q')
-    def model_q(self):
-        """ Action values for all (discrete) actions """
-        # observations = self.get('rollout:observations')
-        # # This mean of last dimension collapses the histogram/calculates mean reward
-        # return self.model(observations).mean(dim=-1)
-        raise NotImplementedError
-
-    @Evaluator.provides('model:q_dist')
-    def model_q_dist(self):
-        """ Action values for all (discrete) actions """
-        observations = self.get('rollout:observations')
-        # This mean of last dimension collapses the histogram/calculates mean reward
-        return self.model(observations)
-
-    @Evaluator.provides('model:action:q')
-    def model_action_q(self):
-        """ Action values for selected actions in the rollout """
-        raise NotImplementedError
-
-    @Evaluator.provides('model:action:q_dist')
-    def model_action_q_dist(self):
-        """ Action values for selected actions in the rollout """
-        q = self.get('model:q_dist')
-        actions = self.get('rollout:actions')
-        return q[range(q.size(0)), actions]
-
-    @Evaluator.provides('model:q_next')
-    def model_q_next(self):
-        """ Action values for all (discrete) actions """
-        raise NotImplementedError
-
-    @Evaluator.provides('model:q_dist_next')
-    def model_q_dist_next(self):
-        """ Action values for all (discrete) actions """
-        observations = self.get('rollout:observations_next')
-        # This mean of last dimension collapses the histogram/calculates mean reward
-        return self.model(observations)
-
-
-class QDistributionalModel(RlModel):
-    """
-    A deterministic greedy action-value model that learns a value function distribution rather than
-    just an expectation.
-    Supports only discrete action spaces (ones that can be enumerated)
-    """
-    def __init__(self, input_block: BackboneModel, backbone: LinearBackboneModel, action_space: gym.Space,
-                 vmin: float, vmax: float, atoms: int=1):
-        super().__init__()
-
-        self.action_space = action_space
-
-        self.input_block = input_block
-        self.backbone = backbone
-
-        self.q_head = QDistributionalHead(
-            input_dim=backbone.output_dim, action_space=action_space,
-            vmin=vmin, vmax=vmax,
-            atoms=atoms
-        )
-
-    def reset_weights(self):
-        """ Initialize weights to reasonable defaults """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.q_head.reset_weights()
-
-    def forward(self, observations):
-        """ Model forward pass """
-        input_data = self.input_block(observations)
-        base_output = self.backbone(input_data)
-        log_histogram = self.q_head(base_output)
-        return log_histogram
-
-    def histogram_info(self):
-        """ Return extra information about histogram """
-        return self.q_head.histogram_info()
-
-    def step(self, observations):
-        """ Sample action from an action space for given state """
-        log_histogram = self(observations)
-        actions = self.q_head.sample(log_histogram)
-
-        return {
-            'actions': actions,
-            'log_histogram': log_histogram
-        }
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return QDistributionalModelEvaluator(self, rollout)
-
-
-class QDistributionalModelFactory(ModelFactory):
-    """ Factory class for q-learning models """
-    def __init__(self, input_block: ModelFactory, backbone: ModelFactory, vmin: float, vmax: float, atoms: int):
-        self.input_block = input_block
-        self.backbone = backbone
-        self.vmin = vmin
-        self.vmax = vmax
-        self.atoms = atoms
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return QDistributionalModel(
-            input_block=input_block,
-            backbone=backbone,
-            action_space=extra_args['action_space'],
-            vmin=self.vmin,
-            vmax=self.vmax,
-            atoms=self.atoms
-        )
-
-
-def create(backbone: ModelFactory, vmin: float, vmax: float, atoms: int,
-           input_block: typing.Optional[ModelFactory]=None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return QDistributionalModelFactory(
-        input_block=input_block, backbone=backbone,
-        vmin=vmin,
-        vmax=vmax,
-        atoms=atoms
-    )
diff --git a/vel/rl/models/q_dueling_model.py b/vel/rl/models/q_dueling_model.py
deleted file mode 100644
index 09d8518b..00000000
--- a/vel/rl/models/q_dueling_model.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import gym
-import typing
-
-from vel.api import LinearBackboneModel, Model, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, Evaluator
-from vel.rl.modules.q_dueling_head import QDuelingHead
-from vel.rl.models.q_model import QModelEvaluator
-
-
-class QDuelingModel(Model):
-    """
-    Deterministic greedy action-value model with dueling heads (kind of actor and critic)
-    Supports only discrete action spaces (ones that can be enumerated)
-    """
-
-    def __init__(self, input_block: BackboneModel, backbone: LinearBackboneModel, action_space: gym.Space):
-        super().__init__()
-
-        self.action_space = action_space
-
-        self.input_block = input_block
-        self.backbone = backbone
-        self.q_head = QDuelingHead(input_dim=backbone.output_dim, action_space=action_space)
-
-    def forward(self, observations):
-        """ Model forward pass """
-        observations = self.input_block(observations)
-        advantage_features, value_features = self.backbone(observations)
-        q_values = self.q_head(advantage_features, value_features)
-
-        return q_values
-
-    def reset_weights(self):
-        """ Initialize weights to reasonable defaults """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.q_head.reset_weights()
-
-    def step(self, observations):
-        """ Sample action from an action space for given state """
-        q_values = self(observations)
-
-        return {
-            'actions': self.q_head.sample(q_values),
-            'q': q_values
-        }
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return QModelEvaluator(self, rollout)
-
-
-class QDuelingModelFactory(ModelFactory):
-    """ Factory class for policy gradient models """
-    def __init__(self, input_block: ModelFactory, backbone: ModelFactory):
-        self.input_block = input_block
-        self.backbone = backbone
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return QDuelingModel(input_block, backbone, extra_args['action_space'])
-
-
-def create(backbone: ModelFactory, input_block: typing.Optional[ModelFactory]=None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return QDuelingModelFactory(input_block=input_block, backbone=backbone)
diff --git a/vel/rl/models/q_model.py b/vel/rl/models/q_model.py
deleted file mode 100644
index 208137e9..00000000
--- a/vel/rl/models/q_model.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import gym
-import typing
-
-from vel.api import LinearBackboneModel, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, RlModel, Evaluator
-from vel.rl.modules.q_head import QHead
-
-
-class QModelEvaluator(Evaluator):
-    """ Evaluate simple q-model """
-    def __init__(self, model: 'QModel', rollout: Rollout):
-        super().__init__(rollout)
-        self.model = model
-
-    @Evaluator.provides('model:q')
-    def model_q(self):
-        """ Action values for all (discrete) actions """
-        observations = self.get('rollout:observations')
-        return self.model(observations)
-
-    @Evaluator.provides('model:action:q')
-    def model_action_q(self):
-        """ Action values for selected actions in the rollout """
-        q = self.get('model:q')
-        actions = self.get('rollout:actions')
-        return q.gather(1, actions.unsqueeze(1)).squeeze(1)
-
-    @Evaluator.provides('model:q_next')
-    def model_q_next(self):
-        """ Action values for all (discrete) actions """
-        observations = self.get('rollout:observations_next')
-        return self.model(observations)
-
-
-class QModel(RlModel):
-    """
-    Simple deterministic greedy action-value model.
-    Supports only discrete action spaces (ones that can be enumerated)
-    """
-    def __init__(self, input_block: BackboneModel, backbone: LinearBackboneModel, action_space: gym.Space):
-        super().__init__()
-
-        self.action_space = action_space
-
-        self.input_block = input_block
-        self.backbone = backbone
-        self.q_head = QHead(input_dim=backbone.output_dim, action_space=action_space)
-
-    def reset_weights(self):
-        """ Initialize weights to reasonable defaults """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.q_head.reset_weights()
-
-    def forward(self, observations):
-        """ Model forward pass """
-        observations = self.input_block(observations)
-        base_output = self.backbone(observations)
-        q_values = self.q_head(base_output)
-        return q_values
-
-    def step(self, observations):
-        """ Sample action from an action space for given state """
-        q_values = self(observations)
-        actions = self.q_head.sample(q_values)
-
-        return {
-            'actions': actions,
-            'q': q_values
-        }
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return QModelEvaluator(self, rollout)
-
-
-class QModelFactory(ModelFactory):
-    """ Factory class for q-learning models """
-    def __init__(self, input_block: ModelFactory, backbone: ModelFactory):
-        self.input_block = input_block
-        self.backbone = backbone
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return QModel(input_block, backbone, extra_args['action_space'])
-
-
-def create(backbone: ModelFactory, input_block: typing.Optional[ModelFactory]=None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return QModelFactory(input_block=input_block, backbone=backbone)
diff --git a/vel/rl/models/q_noisy_model.py b/vel/rl/models/q_noisy_model.py
deleted file mode 100644
index cfc3e491..00000000
--- a/vel/rl/models/q_noisy_model.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import gym
-import typing
-
-from vel.api import LinearBackboneModel, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, RlModel, Evaluator
-from vel.rl.models.q_model import QModelEvaluator
-from vel.rl.modules.q_noisy_head import QNoisyHead
-
-
-class NoisyQModel(RlModel):
-    """
-    NoisyNets action-value model.
-    Supports only discrete action spaces (ones that can be enumerated)
-    """
-
-    def __init__(self, input_block: BackboneModel, backbone: LinearBackboneModel, action_space: gym.Space,
-                 initial_std_dev=0.4, factorized_noise=True):
-        super().__init__()
-
-        self.action_space = action_space
-
-        self.input_block = input_block
-        self.backbone = backbone
-        self.q_head = QNoisyHead(
-            input_dim=backbone.output_dim, action_space=action_space, initial_std_dev=initial_std_dev,
-            factorized_noise=factorized_noise
-        )
-
-    def reset_weights(self):
-        """ Initialize weights to reasonable defaults """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.q_head.reset_weights()
-
-    def forward(self, observations):
-        """ Model forward pass """
-        observations = self.input_block(observations)
-        base_output = self.backbone(observations)
-        q_values = self.q_head(base_output)
-        return q_values
-
-    def step(self, observations):
-        """ Sample action from an action space for given state """
-        q_values = self(observations)
-        actions = self.q_head.sample(q_values)
-
-        return {
-            'actions': actions,
-            'q': q_values
-        }
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return QModelEvaluator(self, rollout)
-
-
-class NoisyQModelFactory(ModelFactory):
-    """ Factory class for q-learning models """
-    def __init__(self, input_block: ModelFactory, backbone: ModelFactory, initial_std_dev=0.4, factorized_noise=True):
-        self.initial_std_dev = initial_std_dev
-        self.factorized_noise = factorized_noise
-
-        self.input_block = input_block
-        self.backbone = backbone
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return NoisyQModel(
-            input_block, backbone, extra_args['action_space'], initial_std_dev=self.initial_std_dev,
-            factorized_noise=self.factorized_noise
-        )
-
-
-def create(backbone: ModelFactory, input_block: typing.Optional[ModelFactory]=None, initial_std_dev=0.4,
-           factorized_noise=True):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return NoisyQModelFactory(
-        input_block=input_block, backbone=backbone, initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
-    )
diff --git a/vel/rl/models/q_rainbow_model.py b/vel/rl/models/q_rainbow_model.py
deleted file mode 100644
index 3e5aea33..00000000
--- a/vel/rl/models/q_rainbow_model.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import gym
-import typing
-
-from vel.api import LinearBackboneModel, Model, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, Evaluator
-from vel.rl.models.q_distributional_model import QDistributionalModelEvaluator
-from vel.rl.modules.q_distributional_noisy_dueling_head import QDistributionalNoisyDuelingHead
-
-
-class QRainbowModel(Model):
-    """
-    A deterministic greedy action-value model.
-    Includes following commonly known modifications:
-    - Distributional Q-Learning
-    - Dueling architecture
-    - Noisy Nets
-    """
-
-    def __init__(self, input_block: BackboneModel, backbone: LinearBackboneModel, action_space: gym.Space, vmin: float,
-                 vmax: float, atoms: int = 1, initial_std_dev: float = 0.4, factorized_noise: bool = True):
-        super().__init__()
-
-        self.action_space = action_space
-
-        self.input_block = input_block
-        self.backbone = backbone
-
-        self.q_head = QDistributionalNoisyDuelingHead(
-            input_dim=backbone.output_dim,
-            action_space=action_space,
-            vmin=vmin, vmax=vmax, atoms=atoms,
-            initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
-        )
-
-    def reset_weights(self):
-        """ Initialize weights to reasonable defaults """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.q_head.reset_weights()
-
-    def forward(self, observations):
-        """ Model forward pass """
-        input_data = self.input_block(observations)
-        advantage_features, value_features = self.backbone(input_data)
-        log_histogram = self.q_head(advantage_features, value_features)
-        return log_histogram
-
-    def histogram_info(self):
-        """ Return extra information about histogram """
-        return self.q_head.histogram_info()
-
-    def step(self, observations):
-        """ Sample action from an action space for given state """
-        log_histogram = self(observations)
-        actions = self.q_head.sample(log_histogram)
-
-        return {
-            'actions': actions,
-            'log_histogram': log_histogram
-        }
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return QDistributionalModelEvaluator(self, rollout)
-
-
-class QDistributionalModelFactory(ModelFactory):
-    """ Factory class for q-learning models """
-    def __init__(self, input_block: ModelFactory, backbone: ModelFactory, vmin: float, vmax: float, atoms: int,
-                 initial_std_dev: float = 0.4, factorized_noise: bool = True):
-        self.input_block = input_block
-        self.backbone = backbone
-        self.vmin = vmin
-        self.vmax = vmax
-        self.atoms = atoms
-        self.initial_std_dev = initial_std_dev
-        self.factorized_noise = factorized_noise
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return QRainbowModel(
-            input_block=input_block,
-            backbone=backbone,
-            action_space=extra_args['action_space'],
-            vmin=self.vmin,
-            vmax=self.vmax,
-            atoms=self.atoms,
-            initial_std_dev=self.initial_std_dev,
-            factorized_noise=self.factorized_noise
-        )
-
-
-def create(backbone: ModelFactory, vmin: float, vmax: float, atoms: int, initial_std_dev: float = 0.4,
-           factorized_noise: bool = True, input_block: typing.Optional[ModelFactory] = None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return QDistributionalModelFactory(
-        input_block=input_block, backbone=backbone,
-        vmin=vmin,
-        vmax=vmax,
-        atoms=atoms,
-        initial_std_dev=initial_std_dev,
-        factorized_noise=factorized_noise
-    )
diff --git a/vel/rl/models/q_stochastic_policy_model.py b/vel/rl/models/q_stochastic_policy_model.py
deleted file mode 100644
index 929dc3b9..00000000
--- a/vel/rl/models/q_stochastic_policy_model.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import gym
-import torch
-import typing
-
-from vel.api import LinearBackboneModel, Model, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, Evaluator
-from vel.rl.modules.action_head import ActionHead
-from vel.rl.modules.q_head import QHead
-
-
-class QStochasticPolicyEvaluator(Evaluator):
-    """ Evaluator for QPolicyGradientModel """
-    def __init__(self, model: 'QStochasticPolicyModel', rollout: Rollout):
-        super().__init__(rollout)
-
-        self.model = model
-
-        observations = self.get('rollout:observations')
-        logprobs, q = model(observations)
-
-        self.provide('model:logprobs', logprobs)
-        self.provide('model:q', q)
-
-    @Evaluator.provides('model:action:logprobs')
-    def model_action_logprobs(self):
-        actions = self.get('rollout_actions')
-        logprobs = self.get('model:logprobs')
-        return self.model.action_head.logprob(actions, logprobs)
-
-
-class QStochasticPolicyModel(Model):
-    """
-    A policy gradient model with an action-value critic head (instead of more common state-value critic head).
-    Supports only discrete action spaces (ones that can be enumerated)
-    """
-
-    def __init__(self, input_block: BackboneModel, backbone: LinearBackboneModel, action_space: gym.Space):
-        super().__init__()
-
-        assert isinstance(action_space, gym.spaces.Discrete)
-
-        self.input_block = input_block
-        self.backbone = backbone
-
-        self.action_head = ActionHead(
-            input_dim=self.backbone.output_dim,
-            action_space=action_space
-        )
-
-        self.q_head = QHead(
-            input_dim=self.backbone.output_dim,
-            action_space=action_space
-        )
-
-    def reset_weights(self):
-        """ Initialize properly model weights """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.action_head.reset_weights()
-        self.q_head.reset_weights()
-
-    def forward(self, observations):
-        """ Calculate model outputs """
-        input_data = self.input_block(observations)
-
-        base_output = self.backbone(input_data)
-        policy_params = self.action_head(base_output)
-
-        q = self.q_head(base_output)
-
-        return policy_params, q
-
-    def step(self, observation, argmax_sampling=False):
-        """ Select actions based on model's output """
-        policy_params, q = self(observation)
-        actions = self.action_head.sample(policy_params, argmax_sampling=argmax_sampling)
-
-        # log probability - we can do that, because we support only discrete action spaces
-        logprobs = self.action_head.logprob(actions, policy_params)
-
-        return {
-            'actions': actions,
-            'q': q,
-            'logprobs': policy_params,
-            'action:logprobs': logprobs
-        }
-
-    def evaluate(self, rollout: Rollout) -> QStochasticPolicyEvaluator:
-        """ Evaluate model on a rollout """
-        return QStochasticPolicyEvaluator(self, rollout)
-
-    def value(self, observation):
-        """ Calculate only value head for given state """
-        policy_params, q = self(observation)
-
-        # Expectation of Q value with respect to action
-        return (torch.exp(policy_params) * q).sum(dim=1)
-
-    def entropy(self, action_logits):
-        """ Entropy of a probability distribution """
-        return self.action_head.entropy(action_logits)
-
-    def kl_divergence(self, logits_q, logits_p):
-        """ Calculate KL-divergence between two probability distributions """
-        return self.action_head.kl_divergence(logits_q, logits_p)
-
-
-class QStochasticPolicyModelFactory(ModelFactory):
-    """ Factory  class for policy gradient models """
-    def __init__(self, input_block: IdentityFactory, backbone: ModelFactory):
-        self.backbone = backbone
-        self.input_block = input_block
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return QStochasticPolicyModel(input_block, backbone, extra_args['action_space'])
-
-
-def create(backbone: ModelFactory, input_block: typing.Optional[ModelFactory]=None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return QStochasticPolicyModelFactory(input_block=input_block, backbone=backbone)
diff --git a/vel/rl/models/stochastic_policy_model.py b/vel/rl/models/stochastic_policy_model.py
deleted file mode 100644
index 27a7c4c2..00000000
--- a/vel/rl/models/stochastic_policy_model.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import gym
-import typing
-
-from vel.api import LinearBackboneModel, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, Evaluator, RlModel
-from vel.rl.modules.action_head import ActionHead
-from vel.rl.modules.value_head import ValueHead
-
-
-class StochasticPolicyEvaluator(Evaluator):
-    """ Evaluator for a policy gradient model """
-
-    def __init__(self, model: 'StochasticPolicyModel', rollout: Rollout):
-        super().__init__(rollout)
-
-        self.model = model
-
-        policy_params, estimated_values = model(self.rollout.batch_tensor('observations'))
-
-        self.provide('model:policy_params',  policy_params)
-        self.provide('model:values', estimated_values)
-
-    @Evaluator.provides('model:action:logprobs')
-    def model_action_logprobs(self):
-        actions = self.get('rollout:actions')
-        policy_params = self.get('model:policy_params')
-        return self.model.action_head.logprob(actions, policy_params)
-
-    @Evaluator.provides('model:entropy')
-    def model_entropy(self):
-        policy_params = self.get('model:policy_params')
-        return self.model.entropy(policy_params)
-
-
-class StochasticPolicyModel(RlModel):
-    """
-    Most generic policy gradient model class with a set of common actor-critic heads that share a single backbone
-    """
-
-    def __init__(self, input_block: BackboneModel, backbone: LinearBackboneModel, action_space: gym.Space):
-        super().__init__()
-
-        self.input_block = input_block
-        self.backbone = backbone
-        self.action_head = ActionHead(
-            action_space=action_space,
-            input_dim=self.backbone.output_dim
-        )
-        self.value_head = ValueHead(input_dim=self.backbone.output_dim)
-
-    def reset_weights(self):
-        """ Initialize properly model weights """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.action_head.reset_weights()
-        self.value_head.reset_weights()
-
-    def forward(self, observations):
-        """ Calculate model outputs """
-        input_data = self.input_block(observations)
-
-        base_output = self.backbone(input_data)
-
-        action_output = self.action_head(base_output)
-        value_output = self.value_head(base_output)
-
-        return action_output, value_output
-
-    def step(self, observation, argmax_sampling=False):
-        """ Select actions based on model's output """
-        action_pd_params, value_output = self(observation)
-        actions = self.action_head.sample(action_pd_params, argmax_sampling=argmax_sampling)
-
-        # log likelihood of selected action
-        logprobs = self.action_head.logprob(actions, action_pd_params)
-
-        return {
-            'actions': actions,
-            'values': value_output,
-            'action:logprobs': logprobs
-        }
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return StochasticPolicyEvaluator(self, rollout)
-
-    def logprob(self, action_sample, policy_params):
-        """ Calculate - log(prob) of selected actions """
-        return self.action_head.logprob(action_sample, policy_params)
-
-    def value(self, observations):
-        """ Calculate only value head for given state """
-        input_data = self.input_block(observations)
-        base_output = self.backbone(input_data)
-        value_output = self.value_head(base_output)
-        return value_output
-
-    def entropy(self, policy_params):
-        """ Entropy of a probability distribution """
-        return self.action_head.entropy(policy_params)
-
-
-class StochasticPolicyModelFactory(ModelFactory):
-    """ Factory class for policy gradient models """
-    def __init__(self, input_block: IdentityFactory, backbone: ModelFactory):
-        self.backbone = backbone
-        self.input_block = input_block
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return StochasticPolicyModel(input_block, backbone, extra_args['action_space'])
-
-
-def create(backbone: ModelFactory, input_block: typing.Optional[ModelFactory]=None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return StochasticPolicyModelFactory(input_block=input_block, backbone=backbone)
diff --git a/vel/rl/models/stochastic_policy_model_separate.py b/vel/rl/models/stochastic_policy_model_separate.py
deleted file mode 100644
index 50ab5ffd..00000000
--- a/vel/rl/models/stochastic_policy_model_separate.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import gym
-import itertools as it
-import typing
-
-from vel.api import LinearBackboneModel, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, RlModel, Evaluator
-from vel.rl.modules.action_head import ActionHead
-from vel.rl.modules.value_head import ValueHead
-from vel.rl.models.stochastic_policy_model import StochasticPolicyEvaluator
-
-
-class StochasticPolicyModelSeparate(RlModel):
-    """
-    Policy gradient model class with an actor and critic heads that don't share a backbone
-    """
-
-    def __init__(self, input_block: BackboneModel,
-                 policy_backbone: LinearBackboneModel, value_backbone: LinearBackboneModel,
-                 action_space: gym.Space):
-        super().__init__()
-
-        self.input_block = input_block
-        self.policy_backbone = policy_backbone
-        self.value_backbone = value_backbone
-
-        self.action_head = ActionHead(
-            action_space=action_space,
-            input_dim=self.policy_backbone.output_dim
-        )
-
-        self.value_head = ValueHead(input_dim=self.value_backbone.output_dim)
-
-    def reset_weights(self):
-        """ Initialize properly model weights """
-        self.input_block.reset_weights()
-
-        self.policy_backbone.reset_weights()
-        self.value_backbone.reset_weights()
-
-        self.action_head.reset_weights()
-        self.value_head.reset_weights()
-
-    def forward(self, observations):
-        """ Calculate model outputs """
-        input_data = self.input_block(observations)
-
-        policy_base_output = self.policy_backbone(input_data)
-        value_base_output = self.value_backbone(input_data)
-
-        action_output = self.action_head(policy_base_output)
-        value_output = self.value_head(value_base_output)
-
-        return action_output, value_output
-
-    def step(self, observation, argmax_sampling=False):
-        """ Select actions based on model's output """
-        policy_params, values = self(observation)
-        actions = self.action_head.sample(policy_params, argmax_sampling=argmax_sampling)
-
-        # log likelihood of selected action
-        logprobs = self.action_head.logprob(actions, policy_params)
-
-        return {
-            'actions': actions,
-            'values': values,
-            'action:logprobs': logprobs
-        }
-
-    def policy_parameters(self):
-        """ Parameters of policy """
-        return it.chain(self.policy_backbone.parameters(), self.action_head.parameters())
-
-    def logprob(self, action_sample, policy_params):
-        """ Calculate - log(prob) of selected actions """
-        return self.action_head.logprob(action_sample, policy_params)
-
-    def value(self, observations):
-        """ Calculate only value head for given state """
-        input_data = self.input_block(observations)
-        base_output = self.value_backbone(input_data)
-        value_output = self.value_head(base_output)
-        return value_output
-
-    def policy(self, observations):
-        """ Calculate only action head for given state """
-        input_data = self.input_block(observations)
-        policy_base_output = self.policy_backbone(input_data)
-        policy_params = self.action_head(policy_base_output)
-        return policy_params
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return StochasticPolicyEvaluator(self, rollout)
-
-    def entropy(self, policy_params):
-        """ Entropy of a probability distribution """
-        return self.action_head.entropy(policy_params)
-
-    def kl_divergence(self, pd_q, pd_p):
-        """ Calculate KL-divergence between two probability distributions """
-        return self.action_head.kl_divergence(pd_q, pd_p)
-
-
-class StochasticPolicyModelSeparateFactory(ModelFactory):
-    """ Factory class for policy gradient models """
-    def __init__(self, input_block: ModelFactory, policy_backbone: ModelFactory, value_backbone: ModelFactory):
-        self.input_block = input_block
-        self.policy_backbone = policy_backbone
-        self.value_backbone = value_backbone
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        policy_backbone = self.policy_backbone.instantiate(**extra_args)
-        value_backbone = self.value_backbone.instantiate(**extra_args)
-
-        return StochasticPolicyModelSeparate(input_block, policy_backbone, value_backbone, extra_args['action_space'])
-
-
-def create(policy_backbone: ModelFactory, value_backbone: ModelFactory,
-           input_block: typing.Optional[ModelFactory] = None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return StochasticPolicyModelSeparateFactory(
-        input_block=input_block,
-        policy_backbone=policy_backbone,
-        value_backbone=value_backbone
-    )
diff --git a/vel/rl/models/stochastic_policy_rnn_model.py b/vel/rl/models/stochastic_policy_rnn_model.py
deleted file mode 100644
index 78afc436..00000000
--- a/vel/rl/models/stochastic_policy_rnn_model.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import gym
-import torch
-import typing
-
-from vel.api import RnnLinearBackboneModel, ModelFactory, BackboneModel
-from vel.modules.input.identity import IdentityFactory
-from vel.rl.api import Rollout, Trajectories, Evaluator, RlRnnModel
-from vel.rl.modules.action_head import ActionHead
-from vel.rl.modules.value_head import ValueHead
-
-
-class StochasticPolicyRnnEvaluator(Evaluator):
-    """ Evaluate recurrent model from initial state """
-
-    def __init__(self, model: 'StochasticPolicyRnnModel', rollout: Rollout):
-        assert isinstance(rollout, Trajectories), "For an RNN model, we must evaluate trajectories"
-        super().__init__(rollout)
-
-        self.model = model
-
-        observation_trajectories = rollout.transition_tensors['observations']
-        hidden_state = rollout.rollout_tensors['initial_hidden_state']
-
-        action_accumulator = []
-        value_accumulator = []
-
-        # Evaluate recurrent network step by step
-        for i in range(observation_trajectories.size(0)):
-            action_output, value_output, hidden_state = model(observation_trajectories[i], hidden_state)
-            action_accumulator.append(action_output)
-            value_accumulator.append(value_output)
-
-        policy_params = torch.cat(action_accumulator, dim=0)
-        estimated_values = torch.cat(value_accumulator, dim=0)
-
-        self.provide('model:policy_params', policy_params)
-        self.provide('model:values', estimated_values)
-
-    @Evaluator.provides('model:action:logprobs')
-    def model_action_logprobs(self):
-        actions = self.get('rollout:actions')
-        policy_params = self.get('model:policy_params')
-        return self.model.action_head.logprob(actions, policy_params)
-
-    @Evaluator.provides('model:entropy')
-    def model_entropy(self):
-        policy_params = self.get('model:policy_params')
-        return self.model.entropy(policy_params)
-
-
-class StochasticPolicyRnnModel(RlRnnModel):
-    """
-    Most generic policy gradient model class with a set of common actor-critic heads that share a single backbone
-    RNN version
-    """
-
-    def __init__(self, input_block: BackboneModel, backbone: RnnLinearBackboneModel, action_space: gym.Space):
-        super().__init__()
-
-        self.input_block = input_block
-        self.backbone = backbone
-
-        self.action_head = ActionHead(
-            action_space=action_space,
-            input_dim=self.backbone.output_dim
-        )
-        self.value_head = ValueHead(input_dim=self.backbone.output_dim)
-
-        assert self.backbone.is_recurrent, "Backbone must be a recurrent model"
-
-    @property
-    def state_dim(self) -> int:
-        """ Dimension of model state """
-        return self.backbone.state_dim
-
-    def reset_weights(self):
-        """ Initialize properly model weights """
-        self.input_block.reset_weights()
-        self.backbone.reset_weights()
-        self.action_head.reset_weights()
-        self.value_head.reset_weights()
-
-    def forward(self, observations, state):
-        """ Calculate model outputs """
-        input_data = self.input_block(observations)
-        base_output, new_state = self.backbone(input_data, state=state)
-
-        action_output = self.action_head(base_output)
-        value_output = self.value_head(base_output)
-
-        return action_output, value_output, new_state
-
-    def step(self, observations, state, argmax_sampling=False):
-        """ Select actions based on model's output """
-        action_pd_params, value_output, new_state = self(observations, state)
-        actions = self.action_head.sample(action_pd_params, argmax_sampling=argmax_sampling)
-
-        # log likelihood of selected action
-        logprobs = self.action_head.logprob(actions, action_pd_params)
-
-        return {
-            'actions': actions,
-            'values': value_output,
-            'action:logprobs': logprobs,
-            'state': new_state
-        }
-
-    def evaluate(self, rollout: Rollout) -> Evaluator:
-        """ Evaluate model on a rollout """
-        return StochasticPolicyRnnEvaluator(self, rollout)
-
-    def logprob(self, action_sample, policy_params):
-        """ Calculate - log(prob) of selected actions """
-        return self.action_head.logprob(action_sample, policy_params)
-
-    def value(self, observations, state):
-        """ Calculate only value head for given state """
-        input_data = self.input_block(observations)
-
-        base_output, new_state = self.backbone(input_data, state)
-        value_output = self.value_head(base_output)
-
-        return value_output
-
-    def entropy(self, action_pd_params):
-        """ Entropy of a probability distribution """
-        return self.action_head.entropy(action_pd_params)
-
-
-class PolicyGradientRnnModelFactory(ModelFactory):
-    """ Factory class for policy gradient models """
-    def __init__(self, input_block: ModelFactory, backbone: ModelFactory):
-        self.input_block = input_block
-        self.backbone = backbone
-
-    def instantiate(self, **extra_args):
-        """ Instantiate the model """
-        input_block = self.input_block.instantiate()
-        backbone = self.backbone.instantiate(**extra_args)
-
-        return StochasticPolicyRnnModel(input_block, backbone, extra_args['action_space'])
-
-
-def create(backbone: ModelFactory, input_block: typing.Optional[ModelFactory]=None):
-    """ Vel factory function """
-    if input_block is None:
-        input_block = IdentityFactory()
-
-    return PolicyGradientRnnModelFactory(
-        input_block=input_block,
-        backbone=backbone
-    )
diff --git a/vel/rl/module/__init__.py b/vel/rl/module/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/rl/module/actor_critic_policy.py b/vel/rl/module/actor_critic_policy.py
new file mode 100644
index 00000000..bc4a5eae
--- /dev/null
+++ b/vel/rl/module/actor_critic_policy.py
@@ -0,0 +1,96 @@
+import itertools as it
+
+import gym
+
+from vel.api import VModule, BackboneModule
+
+
+from vel.rl.module.head.deterministic_action_head import DeterministicActionHead
+from vel.rl.module.head.deterministic_critic_head import DeterministicCriticHead
+
+
+class ActorCriticPolicy(VModule):
+    """ Deterministic Policy Gradient - model """
+
+    def __init__(self, input_net: BackboneModule, policy_net: BackboneModule,
+                 value_net: BackboneModule, action_space: gym.Space):
+        super().__init__()
+
+        self.input_net = input_net
+        self.policy_backbone = policy_net
+        self.value_backbone = value_net
+
+        self.action_head = DeterministicActionHead(
+            input_dim=self.policy_backbone.size_hints().assert_single().last(),
+            action_space=action_space
+        )
+
+        self.critic_head = DeterministicCriticHead(
+            input_dim=self.value_backbone.size_hints().assert_single().last()
+        )
+
+    def layer_groups(self):
+        """ Grouped layers for optimization purposes """
+        return [
+            [self.input_net, self.policy_backbone, self.action_head],
+            [self.input_net, self.value_backbone, self.critic_head],
+        ]
+
+    def grouped_parameters(self):
+        """ Return iterable of pairs (group, parameters) """
+        return it.chain(
+            self.input_net.grouped_parameters(),
+            self.policy_backbone.grouped_parameters(),
+            self.value_backbone.grouped_parameters(),
+            [("actor", self.action_head.parameters())],
+            [("critic", self.critic_head.parameters())],
+        )
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.input_net.reset_weights()
+        self.policy_backbone.reset_weights()
+        self.value_backbone.reset_weights()
+        self.action_head.reset_weights()
+        self.critic_head.reset_weights()
+
+    def forward(self, observations, input_actions=None):
+        """ Calculate model outputs """
+        observations = self.input_net(observations)
+
+        if input_actions is not None:
+            actions = input_actions
+
+            value_hidden = self.value_backbone((observations, actions))
+
+            values = self.critic_head(value_hidden)
+        else:
+            policy_hidden = self.policy_backbone(observations)
+            actions = self.action_head(policy_hidden)
+
+            # value_input = torch.cat([observations, actions], dim=1)
+            value_hidden = self.value_backbone((observations, actions))
+
+            values = self.critic_head(value_hidden)
+
+        return actions, values
+
+    def policy_parameters(self):
+        """ Parameters of policy """
+        return it.chain(self.input_net(), self.policy_backbone.parameters(), self.action_head.parameters())
+
+    def value_parameters(self):
+        """ Parameters of policy """
+        return it.chain(self.input_net(), self.value_backbone.parameters(), self.critic_head.parameters())
+
+    def value(self, observation, input_actions=None):
+        """ Calculate value for given state """
+        action, value = self(observation, input_actions)
+        return value
+
+    def action(self, observations):
+        """ Calculate value for given state """
+        observations = self.input_net(observations)
+        policy_hidden = self.policy_backbone(observations)
+        action = self.action_head(policy_hidden)
+        return action
diff --git a/vel/rl/module/head/__init__.py b/vel/rl/module/head/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/rl/modules/deterministic_action_head.py b/vel/rl/module/head/deterministic_action_head.py
similarity index 100%
rename from vel/rl/modules/deterministic_action_head.py
rename to vel/rl/module/head/deterministic_action_head.py
diff --git a/vel/rl/modules/deterministic_critic_head.py b/vel/rl/module/head/deterministic_critic_head.py
similarity index 100%
rename from vel/rl/modules/deterministic_critic_head.py
rename to vel/rl/module/head/deterministic_critic_head.py
diff --git a/vel/rl/modules/q_distributional_head.py b/vel/rl/module/head/q_distributional_head.py
similarity index 100%
rename from vel/rl/modules/q_distributional_head.py
rename to vel/rl/module/head/q_distributional_head.py
diff --git a/vel/rl/modules/q_distributional_noisy_dueling_head.py b/vel/rl/module/head/q_distributional_noisy_dueling_head.py
similarity index 72%
rename from vel/rl/modules/q_distributional_noisy_dueling_head.py
rename to vel/rl/module/head/q_distributional_noisy_dueling_head.py
index 4c5c30bb..8cc39aa9 100644
--- a/vel/rl/modules/q_distributional_noisy_dueling_head.py
+++ b/vel/rl/module/head/q_distributional_noisy_dueling_head.py
@@ -5,12 +5,12 @@
 import torch.nn.functional as F
 
 
-from vel.rl.modules.noisy_linear import NoisyLinear
+from vel.rl.module.noisy_linear import NoisyLinear
 
 
 class QDistributionalNoisyDuelingHead(nn.Module):
     """ Network head calculating Q-function value for each (discrete) action. """
-    def __init__(self, input_dim, action_space, vmin: float, vmax: float, atoms: int = 1,
+    def __init__(self, val_input_dim, adv_input_dim, action_space, vmin: float, vmax: float, atoms: int = 1,
                  initial_std_dev: float = 0.4, factorized_noise: bool = True):
         super().__init__()
 
@@ -28,11 +28,12 @@ def __init__(self, input_dim, action_space, vmin: float, vmax: float, atoms: int
         self.atom_delta = (self.vmax - self.vmin) / (self.atoms - 1)
 
         self.linear_layer_advantage = NoisyLinear(
-            input_dim, self.action_size * self.atoms, initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
+            adv_input_dim, self.action_size * self.atoms, initial_std_dev=initial_std_dev,
+            factorized_noise=factorized_noise
         )
 
         self.linear_layer_value = NoisyLinear(
-            input_dim, self.atoms, initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
+            val_input_dim, self.atoms, initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
         )
 
         self.register_buffer('support_atoms', torch.linspace(self.vmin, self.vmax, self.atoms))
@@ -51,9 +52,14 @@ def reset_weights(self):
         self.linear_layer_advantage.reset_weights()
         self.linear_layer_value.reset_weights()
 
-    def forward(self, advantage_features, value_features):
-        adv = self.linear_layer_advantage(advantage_features).view(-1, self.action_size, self.atoms)
-        val = self.linear_layer_value(value_features).view(-1, 1, self.atoms)
+    def forward(self, advantage_features, value_features, deterministic=False):
+        adv = self.linear_layer_advantage(
+            advantage_features, deterministic=deterministic
+        ).view(-1, self.action_size, self.atoms)
+
+        val = self.linear_layer_value(
+            value_features, deterministic=deterministic
+        ).view(-1, 1, self.atoms)
 
         # I'm quite unsure if this is the right way to combine these, but this is what paper seems to be suggesting
         # and I don't know any better way.
diff --git a/vel/rl/modules/q_dueling_head.py b/vel/rl/module/head/q_dueling_head.py
similarity index 83%
rename from vel/rl/modules/q_dueling_head.py
rename to vel/rl/module/head/q_dueling_head.py
index 78a9d6ea..600e3564 100644
--- a/vel/rl/modules/q_dueling_head.py
+++ b/vel/rl/module/head/q_dueling_head.py
@@ -6,14 +6,14 @@
 
 class QDuelingHead(nn.Module):
     """ Network head calculating Q-function value for each (discrete) action using two separate inputs. """
-    def __init__(self, input_dim, action_space):
+    def __init__(self, val_input_dim, adv_input_dim, action_space):
         super().__init__()
 
         # Q-function requires a discrete action space
         assert isinstance(action_space, spaces.Discrete)
 
-        self.linear_layer_advantage = nn.Linear(input_dim, action_space.n)
-        self.linear_layer_value = nn.Linear(input_dim, 1)
+        self.linear_layer_value = nn.Linear(val_input_dim, 1)
+        self.linear_layer_advantage = nn.Linear(adv_input_dim, action_space.n)
         self.action_space = action_space
 
     def reset_weights(self):
diff --git a/vel/rl/modules/q_head.py b/vel/rl/module/head/q_head.py
similarity index 99%
rename from vel/rl/modules/q_head.py
rename to vel/rl/module/head/q_head.py
index 52abaaeb..00431c2f 100644
--- a/vel/rl/modules/q_head.py
+++ b/vel/rl/module/head/q_head.py
@@ -26,4 +26,3 @@ def forward(self, input_data):
     def sample(self, q_values):
         """ Sample from epsilon-greedy strategy with given q-values """
         return q_values.argmax(dim=1)
-
diff --git a/vel/rl/modules/q_noisy_head.py b/vel/rl/module/head/q_noisy_head.py
similarity index 94%
rename from vel/rl/modules/q_noisy_head.py
rename to vel/rl/module/head/q_noisy_head.py
index 63f510d6..8b171e1c 100644
--- a/vel/rl/modules/q_noisy_head.py
+++ b/vel/rl/module/head/q_noisy_head.py
@@ -2,7 +2,7 @@
 
 import gym.spaces as spaces
 
-from vel.rl.modules.noisy_linear import NoisyLinear
+from vel.rl.module.noisy_linear import NoisyLinear
 
 
 class QNoisyHead(nn.Module):
diff --git a/vel/rl/modules/action_head.py b/vel/rl/module/head/stochastic_action_head.py
similarity index 69%
rename from vel/rl/modules/action_head.py
rename to vel/rl/module/head/stochastic_action_head.py
index 8cd6b6ba..d0ce774f 100644
--- a/vel/rl/modules/action_head.py
+++ b/vel/rl/module/head/stochastic_action_head.py
@@ -1,3 +1,4 @@
+import gym
 import numpy as np
 
 import torch
@@ -32,12 +33,12 @@ def forward(self, input_data):
 
         return torch.stack([means, log_std_tile], dim=-1)
 
-    def sample(self, params, argmax_sampling=False):
+    def sample(self, params, deterministic=False):
         """ Sample from a probability space of all actions """
         means = params[:, :, 0]
         log_std = params[:, :, 1]
 
-        if argmax_sampling:
+        if deterministic:
             return means
         else:
             return torch.randn_like(means) * torch.exp(log_std) + means
@@ -105,11 +106,12 @@ def logprob(self, actions, action_logits):
         neg_log_prob = F.nll_loss(action_logits, actions, reduction='none')
         return -neg_log_prob
 
-    def sample(self, logits, argmax_sampling=False):
+    def sample(self, logits, deterministic=False):
         """ Sample from a probability space of all actions """
-        if argmax_sampling:
+        if deterministic:
             return torch.argmax(logits, dim=-1)
         else:
+            # Gumbel-Softmax trick
             u = torch.rand_like(logits)
             return torch.argmax(logits - torch.log(-torch.log(u)), dim=-1)
 
@@ -133,47 +135,12 @@ def kl_divergence(self, logits_q, logits_p):
         return (torch.exp(logits_q) * (logits_q - logits_p)).sum(1, keepdim=True)
 
 
-class ActionHead(nn.Module):
-    """
-    Network head for action determination. Returns probability distribution parametrization
-    """
-
-    def __init__(self, input_dim, action_space):
-        super().__init__()
-
-        self.action_space = action_space
-
-        if isinstance(action_space, spaces.Box):
-            assert len(action_space.shape) == 1
-            self.head = DiagGaussianActionHead(input_dim, action_space.shape[0])
-        elif isinstance(action_space, spaces.Discrete):
-            self.head = CategoricalActionHead(input_dim, action_space.n)
-        # elif isinstance(action_space, spaces.MultiDiscrete):
-        #     return MultiCategoricalPdType(action_space.nvec)
-        # elif isinstance(action_space, spaces.MultiBinary):
-        #     return BernoulliPdType(action_space.n)
-        else:
-            raise NotImplementedError
-
-    def forward(self, input_data):
-        return self.head(input_data)
-
-    def sample(self, policy_params, **kwargs):
-        """ Sample from a probability space of all actions """
-        return self.head.sample(policy_params, **kwargs)
-
-    def reset_weights(self):
-        """ Initialize weights to sane defaults """
-        self.head.reset_weights()
-
-    def entropy(self, policy_params):
-        """ Entropy calculation - sum probs * log(probs) """
-        return self.head.entropy(policy_params)
-
-    def kl_divergence(self, params_q, params_p):
-        """ Kullback–Leibler divergence between two sets of parameters """
-        return self.head.kl_divergence(params_q, params_p)
-
-    def logprob(self, action_sample, policy_params):
-        """ - log probabilty of selected actions """
-        return self.head.logprob(action_sample, policy_params)
+def make_stockastic_action_head(input_dim: int, action_space: gym.Space):
+    """ Instantiate stochastic action space relevant for the task """
+    if isinstance(action_space, spaces.Box):
+        assert len(action_space.shape) == 1
+        return DiagGaussianActionHead(input_dim, action_space.shape[0])
+    elif isinstance(action_space, spaces.Discrete):
+        return CategoricalActionHead(input_dim, action_space.n)
+    else:
+        raise NotImplementedError
diff --git a/vel/rl/modules/value_head.py b/vel/rl/module/head/value_head.py
similarity index 100%
rename from vel/rl/modules/value_head.py
rename to vel/rl/module/head/value_head.py
diff --git a/vel/rl/module/noise/__init__.py b/vel/rl/module/noise/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/rl/module/noise/eps_greedy.py b/vel/rl/module/noise/eps_greedy.py
new file mode 100644
index 00000000..e48ace91
--- /dev/null
+++ b/vel/rl/module/noise/eps_greedy.py
@@ -0,0 +1,36 @@
+import gym
+import typing
+
+import torch
+
+from vel.api import Schedule, VModule
+from vel.internal.generic_factory import GenericFactory
+
+
+class EpsGreedy(VModule):
+    """ Epsilon-greedy action selection """
+    def __init__(self, action_space: gym.Space):
+        super().__init__()
+
+        self.action_space = action_space
+
+    def forward(self, actions, epsilon, deterministic=False):
+        if deterministic:
+            return actions
+        else:
+            random_samples = torch.randint_like(actions, self.action_space.n)
+            selector = torch.rand_like(random_samples, dtype=torch.float32)
+
+            # Actions with noise applied
+            noisy_actions = torch.where(selector > epsilon, actions, random_samples)
+
+            return noisy_actions
+
+    def reset_training_state(self, dones, batch_info):
+        """ A hook for a model to react when during training episode is finished """
+        pass
+
+
+def create(epsilon: typing.Union[Schedule, float]):
+    """ Vel factory function """
+    return GenericFactory(EpsGreedy, arguments={'epsilon': epsilon})
diff --git a/vel/rl/modules/noise/ou_noise.py b/vel/rl/module/noise/ou_noise.py
similarity index 66%
rename from vel/rl/modules/noise/ou_noise.py
rename to vel/rl/module/noise/ou_noise.py
index be6ea0d8..66bcb065 100644
--- a/vel/rl/modules/noise/ou_noise.py
+++ b/vel/rl/module/noise/ou_noise.py
@@ -1,33 +1,27 @@
-import torch
+import gym
 import numpy as np
-import torch.nn as nn
+import torch
 
-from vel.math.processes import OrnsteinUhlenbeckNoiseProcess
-from vel.internals.generic_factory import GenericFactory
+from vel.api import VModule
+from vel.util.process import OrnsteinUhlenbeckNoiseProcess
+from vel.internal.generic_factory import GenericFactory
 
 
-class OuNoise(nn.Module):
+class OuNoise(VModule):
     """ Ornstein–Uhlenbeck noise process for action noise """
 
-    def __init__(self, std_dev, environment):
+    def __init__(self, std_dev: float, action_space: gym.Space):
         super().__init__()
 
         self.std_dev = std_dev
-        self.action_space = environment.action_space
+        self.action_space = action_space
         self.processes = []
 
         self.register_buffer('low_tensor', torch.from_numpy(self.action_space.low).unsqueeze(0))
         self.register_buffer('high_tensor', torch.from_numpy(self.action_space.high).unsqueeze(0))
 
-    def reset_training_state(self, dones, batch_info):
-        """ A hook for a model to react when during training episode is finished """
-        for idx, done in enumerate(dones):
-            if done > 0.5:
-                self.processes[idx].reset()
-
-    def forward(self, actions, batch_info):
-        """ Return model step after applying noise """
-        while len(self.processes) < actions.shape[0]:
+    def _expand_processes(self, shape):
+        while len(self.processes) < shape:
             len_action_space = self.action_space.shape[-1]
 
             self.processes.append(
@@ -36,8 +30,19 @@ def forward(self, actions, batch_info):
                 )
             )
 
-        noise = torch.from_numpy(np.stack([x() for x in self.processes])).float().to(actions.device)
+    def reset_episodic_state(self, dones):
+        """ A hook for a model to react when during training episode is finished """
+        self._expand_processes(dones.shape[0])
+
+        for idx, done in enumerate(dones.cpu()):
+            if done > 0.5:
+                self.processes[idx].reset()
 
+    def forward(self, actions):
+        """ Return model step after applying noise """
+        self._expand_processes(actions.shape[0])
+
+        noise = torch.from_numpy(np.stack([x() for x in self.processes])).float().to(actions.device)
         return torch.min(torch.max(actions + noise, self.low_tensor), self.high_tensor)
 
 
diff --git a/vel/rl/modules/noisy_linear.py b/vel/rl/module/noisy_linear.py
similarity index 95%
rename from vel/rl/modules/noisy_linear.py
rename to vel/rl/module/noisy_linear.py
index 1fdea082..9a56fa3a 100644
--- a/vel/rl/modules/noisy_linear.py
+++ b/vel/rl/module/noisy_linear.py
@@ -54,8 +54,8 @@ def reset_weights(self):
         self.weight_sigma.data.fill_(self.initial_std_dev / math.sqrt(self.in_features))
         self.bias_sigma.data.fill_(self.initial_std_dev / math.sqrt(self.out_features))
 
-    def forward(self, input_data):
-        if self.training:
+    def forward(self, input_data, deterministic=False):
+        if not deterministic:
             if self.factorized_noise:
                 weight_epsilon, bias_epsilon = factorized_gaussian_noise(
                     self.in_features, self.out_features, device=input_data.device
@@ -82,5 +82,5 @@ def extra_repr(self):
         """
         return (
             f'{self.in_features}, {self.out_features}, initial_std_dev={self.initial_std_dev}, '
-            'factorized_noise={self.factorized_noise} '
+            f'factorized_noise={self.factorized_noise} '
         )
diff --git a/vel/rl/module/q_policy.py b/vel/rl/module/q_policy.py
new file mode 100644
index 00000000..6c18a235
--- /dev/null
+++ b/vel/rl/module/q_policy.py
@@ -0,0 +1,50 @@
+import gym
+
+from vel.api import VModule, BackboneModule
+
+from vel.rl.module.head.q_head import QHead
+from vel.rl.module.head.q_dueling_head import QDuelingHead
+
+
+class QPolicy(VModule):
+    """
+    Simple deterministic greedy action-value model.
+    Supports only discrete action spaces (ones that can be enumerated)
+    """
+    def __init__(self, net: BackboneModule, action_space: gym.Space, dueling_dqn=False):
+        super().__init__()
+
+        self.dueling_dqn = dueling_dqn
+        self.action_space = action_space
+
+        self.net = net
+
+        if self.dueling_dqn:
+            (value_size, adv_size) = self.net.size_hints().assert_tuple(2)
+
+            self.q_head = QDuelingHead(
+                val_input_dim=value_size.last(),
+                adv_input_dim=adv_size.last(),
+                action_space=action_space
+            )
+        else:
+            self.q_head = QHead(
+                input_dim=self.net.size_hints().assert_single(2).last(),
+                action_space=action_space
+            )
+
+    def reset_weights(self):
+        """ Initialize weights to reasonable defaults """
+        self.net.reset_weights()
+        self.q_head.reset_weights()
+
+    def forward(self, observations):
+        """ Model forward pass """
+        if self.dueling_dqn:
+            val_output, adv_output = self.net(observations)
+            q_values = self.q_head(val_output, adv_output)
+        else:
+            base_output = self.net(observations)
+            q_values = self.q_head(base_output)
+
+        return q_values
diff --git a/vel/rl/module/q_stochastic_policy.py b/vel/rl/module/q_stochastic_policy.py
new file mode 100644
index 00000000..29f12107
--- /dev/null
+++ b/vel/rl/module/q_stochastic_policy.py
@@ -0,0 +1,46 @@
+import gym
+
+from vel.api import BackboneModule, VModule
+from vel.rl.module.head.stochastic_action_head import make_stockastic_action_head
+from vel.rl.module.head.q_head import QHead
+
+
+class QStochasticPolicy(VModule):
+    """
+    A policy model with an action-value critic head (instead of more common state-value critic head).
+    Supports only discrete action spaces (ones that can be enumerated)
+    """
+
+    def __init__(self, net: BackboneModule, action_space: gym.Space):
+        super().__init__()
+
+        assert isinstance(action_space, gym.spaces.Discrete)
+
+        self.net = net
+
+        (action_size, value_size) = self.net.size_hints().assert_tuple(2)
+
+        self.action_head = make_stockastic_action_head(
+            input_dim=action_size.last(),
+            action_space=action_space
+        )
+
+        self.q_head = QHead(
+            input_dim=value_size.last(),
+            action_space=action_space
+        )
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+        self.action_head.reset_weights()
+        self.q_head.reset_weights()
+
+    def forward(self, observations):
+        """ Calculate model outputs """
+        action_hidden, q_hidden = self.net(observations)
+        policy_params = self.action_head(action_hidden)
+
+        q = self.q_head(q_hidden)
+
+        return policy_params, q
diff --git a/vel/rl/module/rainbow_policy.py b/vel/rl/module/rainbow_policy.py
new file mode 100644
index 00000000..2b4fe914
--- /dev/null
+++ b/vel/rl/module/rainbow_policy.py
@@ -0,0 +1,56 @@
+import gym
+import torch
+
+from vel.api import VModule, BackboneModule
+from vel.rl.module.head.q_distributional_noisy_dueling_head import QDistributionalNoisyDuelingHead
+
+
+class RainbowPolicy(VModule):
+    """
+    A deterministic greedy action-value model.
+    Includes following commonly known modifications:
+    - Distributional Q-Learning
+    - Dueling architecture
+    - Noisy Nets
+    """
+
+    def __init__(self, net: BackboneModule, action_space: gym.Space, vmin: float, vmax: float,
+                 atoms: int = 1, initial_std_dev: float = 0.4, factorized_noise: bool = True):
+        super().__init__()
+
+        self.net = net
+
+        self.action_space = action_space
+
+        (value_size, adv_size) = self.net.size_hints().assert_tuple(2)
+
+        self.q_head = QDistributionalNoisyDuelingHead(
+            val_input_dim=value_size.last(),
+            adv_input_dim=adv_size.last(),
+            action_space=action_space,
+            vmin=vmin, vmax=vmax, atoms=atoms,
+            initial_std_dev=initial_std_dev, factorized_noise=factorized_noise
+        )
+
+    @property
+    def atom_delta(self) -> float:
+        return self.q_head.atom_delta
+
+    @property
+    def support_atoms(self) -> torch.Tensor:
+        return self.q_head.support_atoms
+
+    def reset_weights(self):
+        """ Initialize weights to reasonable defaults """
+        self.net.reset_weights()
+        self.q_head.reset_weights()
+
+    def forward(self, observations, deterministic=False):
+        """ Model forward pass """
+        advantage_features, value_features = self.net(observations, context={'deterministic': deterministic})
+        log_histogram = self.q_head(advantage_features, value_features)
+        return log_histogram
+
+    def histogram_info(self):
+        """ Return extra information about histogram """
+        return self.q_head.histogram_info()
diff --git a/vel/rl/module/stochastic_policy.py b/vel/rl/module/stochastic_policy.py
new file mode 100644
index 00000000..ec73101d
--- /dev/null
+++ b/vel/rl/module/stochastic_policy.py
@@ -0,0 +1,41 @@
+import gym
+
+from vel.api import VModule, BackboneModule
+
+from vel.rl.module.head.stochastic_action_head import make_stockastic_action_head
+from vel.rl.module.head.value_head import ValueHead
+
+
+class StochasticPolicy(VModule):
+    """
+    Most generic policy gradient model class with a set of common actor-critic heads that share a single backbone
+    """
+
+    def __init__(self, net: BackboneModule, action_space: gym.Space):
+        super().__init__()
+
+        self.net = net
+
+        assert not self.net.is_stateful, "Backbone shouldn't have state"
+
+        (action_size, value_size) = self.net.size_hints().assert_tuple(2)
+
+        self.action_head = make_stockastic_action_head(
+            action_space=action_space,
+            input_dim=action_size.last(),
+        )
+
+        self.value_head = ValueHead(
+            input_dim=value_size.last()
+        )
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+        self.action_head.reset_weights()
+        self.value_head.reset_weights()
+
+    def forward(self, observation):
+        """ Calculate model outputs """
+        action_hidden, value_hidden = self.net(observation)
+        return self.action_head(action_hidden), self.value_head(value_hidden)
diff --git a/vel/rl/module/stochastic_rnn_policy.py b/vel/rl/module/stochastic_rnn_policy.py
new file mode 100644
index 00000000..aa04f927
--- /dev/null
+++ b/vel/rl/module/stochastic_rnn_policy.py
@@ -0,0 +1,73 @@
+import gym
+
+from vel.api import VModule, BackboneModule
+from vel.rl.module.head.stochastic_action_head import make_stockastic_action_head
+from vel.rl.module.head.value_head import ValueHead
+
+
+class StochasticRnnPolicy(VModule):
+    """
+    Most generic policy gradient model class with a set of common actor-critic heads that share a single backbone
+    RNN version
+    """
+
+    def __init__(self, net: BackboneModule, action_space: gym.Space):
+        super().__init__()
+
+        self.net = net
+
+        assert self.net.is_stateful, "Must have a stateful backbone"
+
+        (action_size, value_size) = self.net.size_hints().assert_tuple(2)
+
+        self.action_head = make_stockastic_action_head(
+            action_space=action_space,
+            input_dim=action_size.last(),
+        )
+        self.value_head = ValueHead(
+            input_dim=value_size.last()
+        )
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return True
+
+    def zero_state(self, batch_size):
+        return self.net.zero_state(batch_size)
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+        self.action_head.reset_weights()
+        self.value_head.reset_weights()
+
+    def forward(self, observations, state):
+        """ Calculate model outputs """
+        (action_hidden, value_hidden), new_state = self.net(observations, state=state)
+
+        action_output = self.action_head(action_hidden)
+        value_output = self.value_head(value_hidden)
+
+        return action_output, value_output, new_state
+
+    def reset_state(self, state, dones):
+        """ Reset the state after the episode has been terminated """
+        if (dones > 0).any().item():
+            dones_expanded = dones.unsqueeze(-1)
+
+            zero_state = self.net.zero_state(dones.shape[0])
+
+            out_state = {}
+
+            for key in state:
+                state_item = state[key]
+                zero_state_item = zero_state[key].to(state_item.device)
+
+                final_item = state_item * (1 - dones_expanded) + zero_state_item * dones_expanded
+
+                out_state[key] = final_item
+
+            return out_state
+        else:
+            return state
diff --git a/vel/rl/module/test/__init__.py b/vel/rl/module/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/rl/modules/test/test_action_head.py b/vel/rl/module/test/test_action_head.py
similarity index 98%
rename from vel/rl/modules/test/test_action_head.py
rename to vel/rl/module/test/test_action_head.py
index 5e3ff74c..8d80e3cc 100644
--- a/vel/rl/modules/test/test_action_head.py
+++ b/vel/rl/module/test/test_action_head.py
@@ -7,7 +7,7 @@
 import torch.nn.functional as F
 import torch.distributions as d
 
-from vel.rl.modules.action_head import DiagGaussianActionHead, CategoricalActionHead
+from vel.rl.module.head.stochastic_action_head import DiagGaussianActionHead, CategoricalActionHead
 
 
 def test_sample_diag_gaussian():
diff --git a/vel/rl/modules/noise/eps_greedy.py b/vel/rl/modules/noise/eps_greedy.py
deleted file mode 100644
index b5e6f0c9..00000000
--- a/vel/rl/modules/noise/eps_greedy.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import typing
-
-import torch
-import torch.nn as nn
-
-from vel.api import Schedule
-from vel.internals.generic_factory import GenericFactory
-from vel.schedules.constant import ConstantSchedule
-
-
-class EpsGreedy(nn.Module):
-    """ Epsilon-greedy action selection """
-    def __init__(self, epsilon: typing.Union[Schedule, float], environment):
-        super().__init__()
-
-        if isinstance(epsilon, Schedule):
-            self.epsilon_schedule = epsilon
-        else:
-            self.epsilon_schedule = ConstantSchedule(epsilon)
-
-        self.action_space = environment.action_space
-
-    def forward(self, actions, batch_info=None):
-        if batch_info is None:
-            # Just take final value if there is no batch info
-            epsilon = self.epsilon_schedule.value(1.0)
-        else:
-            epsilon = self.epsilon_schedule.value(batch_info['progress'])
-
-        random_samples = torch.randint_like(actions, self.action_space.n)
-        selector = torch.rand_like(random_samples, dtype=torch.float32)
-
-        # Actions with noise applied
-        noisy_actions = torch.where(selector > epsilon, actions, random_samples)
-
-        return noisy_actions
-
-    def reset_training_state(self, dones, batch_info):
-        """ A hook for a model to react when during training episode is finished """
-        pass
-
-
-def create(epsilon: typing.Union[Schedule, float]):
-    """ Vel factory function """
-    return GenericFactory(EpsGreedy, arguments={'epsilon': epsilon})
diff --git a/vel/rl/policy/__init__.py b/vel/rl/policy/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/rl/policy/a2c.py b/vel/rl/policy/a2c.py
new file mode 100644
index 00000000..ef4cb1d8
--- /dev/null
+++ b/vel/rl/policy/a2c.py
@@ -0,0 +1,151 @@
+import gym
+import torch
+import torch.nn.functional as F
+
+from vel.metric.base import AveragingNamedMetric
+from vel.util.situational import gym_space_to_size_hint
+from vel.util.stats import explained_variance
+from vel.api import ModuleFactory, BatchInfo, BackboneModule
+
+from vel.rl.api import RlPolicy, Rollout, Trajectories
+from vel.rl.discount_bootstrap import discount_bootstrap_gae
+from vel.rl.module.stochastic_policy import StochasticPolicy
+
+
+class A2C(RlPolicy):
+    """ Simplest policy gradient - calculate loss as an advantage of an actor versus value function """
+    def __init__(self, net: BackboneModule, action_space: gym.Space,
+                 entropy_coefficient, value_coefficient, discount_factor: float,
+                 gae_lambda=1.0):
+        super().__init__(discount_factor)
+
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.gae_lambda = gae_lambda
+
+        self.net = StochasticPolicy(net, action_space)
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation)
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        action_pd_params, value_output = self(observation)
+        actions = self.net.action_head.sample(action_pd_params, deterministic=deterministic)
+
+        # log likelihood of selected action
+        logprobs = self.net.action_head.logprob(actions, action_pd_params)
+
+        return {
+            'actions': actions,
+            'values': value_output,
+            'action:logprobs': logprobs
+        }
+
+    def process_rollout(self, rollout: Rollout) -> Rollout:
+        """ Process rollout for optimization before any chunking/shuffling  """
+        assert isinstance(rollout, Trajectories), "A2C requires trajectory rollouts"
+
+        advantages = discount_bootstrap_gae(
+            rewards_buffer=rollout.transition_tensors['rewards'],
+            dones_buffer=rollout.transition_tensors['dones'],
+            values_buffer=rollout.transition_tensors['values'],
+            final_values=rollout.rollout_tensors['final.values'],
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda,
+            number_of_steps=rollout.num_steps
+        )
+
+        returns = advantages + rollout.transition_tensors['values']
+
+        rollout.transition_tensors['advantages'] = advantages
+        rollout.transition_tensors['returns'] = returns
+
+        return rollout
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Calculate loss of the supplied rollout """
+        observations = rollout.batch_tensor('observations')
+
+        actions = rollout.batch_tensor('actions')
+        advantages = rollout.batch_tensor('advantages')
+        returns = rollout.batch_tensor('returns')
+        rollout_values = rollout.batch_tensor('values')
+
+        pd_params, model_values = self(observations)
+
+        log_probs = self.net.action_head.logprob(actions, pd_params)
+        entropy = self.net.action_head.entropy(pd_params)
+
+        # Actual calculations. Pretty trivial
+        policy_loss = -torch.mean(advantages * log_probs)
+        value_loss = 0.5 * F.mse_loss(model_values, returns)
+        policy_entropy = torch.mean(entropy)
+
+        loss_value = (
+            policy_loss - self.entropy_coefficient * policy_entropy + self.value_coefficient * value_loss
+        )
+
+        loss_value.backward()
+
+        return {
+            'policy_loss': policy_loss.item(),
+            'value_loss': value_loss.item(),
+            'policy_entropy': policy_entropy.item(),
+            'advantage_norm': torch.norm(advantages).item(),
+            'explained_variance': explained_variance(returns, rollout_values)
+        }
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("value_loss", scope="model"),
+            AveragingNamedMetric("policy_entropy", scope="model"),
+            AveragingNamedMetric("policy_loss", scope="model"),
+            AveragingNamedMetric("advantage_norm", scope="model"),
+            AveragingNamedMetric("explained_variance", scope="model")
+        ]
+
+
+class A2CFactory(ModuleFactory):
+    """ Factory class for policy gradient models """
+    def __init__(self, net_factory, entropy_coefficient, value_coefficient, discount_factor, gae_lambda=1.0):
+        self.net_factory = net_factory
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.discount_factor = discount_factor
+        self.gae_lambda = gae_lambda
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+
+        return A2C(
+            net=net,
+            action_space=action_space,
+            entropy_coefficient=self.entropy_coefficient,
+            value_coefficient=self.value_coefficient,
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda
+        )
+
+
+def create(net: ModuleFactory, entropy_coefficient, value_coefficient, discount_factor, gae_lambda=1.0):
+    """ Vel factory function """
+    return A2CFactory(
+        net_factory=net,
+        entropy_coefficient=entropy_coefficient,
+        value_coefficient=value_coefficient,
+        discount_factor=discount_factor,
+        gae_lambda=gae_lambda
+    )
diff --git a/vel/rl/policy/a2c_rnn.py b/vel/rl/policy/a2c_rnn.py
new file mode 100644
index 00000000..5ec298de
--- /dev/null
+++ b/vel/rl/policy/a2c_rnn.py
@@ -0,0 +1,192 @@
+import gym
+import torch
+import torch.nn.functional as F
+
+from vel.api import ModuleFactory, BatchInfo, BackboneModule
+from vel.metric.base import AveragingNamedMetric
+from vel.rl.api import RlPolicy, Rollout, Trajectories
+from vel.rl.discount_bootstrap import discount_bootstrap_gae
+from vel.rl.module.stochastic_rnn_policy import StochasticRnnPolicy
+from vel.util.situational import gym_space_to_size_hint
+from vel.util.stats import explained_variance
+
+
+class A2CRnn(RlPolicy):
+    """ Simplest policy gradient - calculate loss as an advantage of an actor versus value function """
+    def __init__(self, net: BackboneModule, action_space: gym.Space,
+                 entropy_coefficient, value_coefficient, discount_factor: float,
+                 gae_lambda=1.0):
+        super().__init__(discount_factor)
+
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.gae_lambda = gae_lambda
+
+        self.net = StochasticRnnPolicy(net, action_space)
+
+        assert self.net.is_stateful, "Policy must be stateful"
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation, state=state)
+
+    def is_stateful(self) -> bool:
+        return self.net.is_stateful
+
+    def zero_state(self, batch_size):
+        return self.net.zero_state(batch_size)
+
+    def reset_state(self, state, dones):
+        return self.net.reset_state(state, dones)
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        action_pd_params, value_output, next_state = self(observation, state=state)
+        actions = self.net.action_head.sample(action_pd_params, deterministic=deterministic)
+
+        # log likelihood of selected action
+        logprobs = self.net.action_head.logprob(actions, action_pd_params)
+
+        return {
+            'action:logprobs': logprobs,
+            'actions': actions,
+            'state': next_state,
+            'values': value_output,
+        }
+
+    def process_rollout(self, rollout: Rollout) -> Rollout:
+        """ Process rollout for optimization before any chunking/shuffling  """
+        assert isinstance(rollout, Trajectories), "A2C requires trajectory rollouts"
+
+        advantages = discount_bootstrap_gae(
+            rewards_buffer=rollout.transition_tensors['rewards'],
+            dones_buffer=rollout.transition_tensors['dones'],
+            values_buffer=rollout.transition_tensors['values'],
+            final_values=rollout.rollout_tensors['final.values'],
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda,
+            number_of_steps=rollout.num_steps
+        )
+
+        returns = advantages + rollout.transition_tensors['values']
+
+        rollout.transition_tensors['advantages'] = advantages
+        rollout.transition_tensors['returns'] = returns
+
+        return rollout
+
+    def _extract_initial_state(self, transition_tensors):
+        """ Extract initial state from the state dictionary """
+        state = {}
+
+        idx = len('state') + 1
+
+        for key, value in transition_tensors.items():
+            if key.startswith('state'):
+                state[key[idx:]] = value[0]
+
+        return state
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Calculate loss of the supplied rollout """
+        assert isinstance(rollout, Trajectories), "For an RNN model, we must evaluate trajectories"
+
+        # rollout values
+        actions = rollout.batch_tensor('actions')
+        advantages = rollout.batch_tensor('advantages')
+        returns = rollout.batch_tensor('returns')
+        rollout_values = rollout.batch_tensor('values')
+
+        # Let's evaluate the model
+        observations = rollout.transition_tensors['observations']
+        hidden_state = self._extract_initial_state(rollout.transition_tensors)
+        dones = rollout.transition_tensors['dones']
+
+        action_accumulator = []
+        value_accumulator = []
+
+        # Evaluate recurrent network step by step
+        for i in range(observations.size(0)):
+            action_output, value_output, hidden_state = self(observations[i], hidden_state)
+            hidden_state = self.reset_state(hidden_state, dones[i])
+
+            action_accumulator.append(action_output)
+            value_accumulator.append(value_output)
+
+        pd_params = torch.cat(action_accumulator, dim=0)
+        model_values = torch.cat(value_accumulator, dim=0)
+
+        log_probs = self.net.action_head.logprob(actions, pd_params)
+        entropy = self.net.action_head.entropy(pd_params)
+
+        # Actual calculations. Pretty trivial
+        policy_loss = -torch.mean(advantages * log_probs)
+        value_loss = 0.5 * F.mse_loss(model_values, returns)
+        policy_entropy = torch.mean(entropy)
+
+        loss_value = (
+            policy_loss - self.entropy_coefficient * policy_entropy + self.value_coefficient * value_loss
+        )
+
+        loss_value.backward()
+
+        return {
+            'policy_loss': policy_loss.item(),
+            'value_loss': value_loss.item(),
+            'policy_entropy': policy_entropy.item(),
+            'advantage_norm': torch.norm(advantages).item(),
+            'explained_variance': explained_variance(returns, rollout_values)
+        }
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("value_loss", scope="model"),
+            AveragingNamedMetric("policy_entropy", scope="model"),
+            AveragingNamedMetric("policy_loss", scope="model"),
+            AveragingNamedMetric("advantage_norm", scope="model"),
+            AveragingNamedMetric("explained_variance", scope="model")
+        ]
+
+
+class A2CRnnFactory(ModuleFactory):
+    """ Factory class for policy gradient models """
+    def __init__(self, net_factory, entropy_coefficient, value_coefficient, discount_factor, gae_lambda=1.0):
+        self.net_factory = net_factory
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.discount_factor = discount_factor
+        self.gae_lambda = gae_lambda
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+
+        return A2CRnn(
+            net=net,
+            action_space=action_space,
+            entropy_coefficient=self.entropy_coefficient,
+            value_coefficient=self.value_coefficient,
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda
+        )
+
+
+def create(net: ModuleFactory, entropy_coefficient, value_coefficient, discount_factor, gae_lambda=1.0):
+    """ Vel factory function """
+    return A2CRnnFactory(
+        net_factory=net,
+        entropy_coefficient=entropy_coefficient,
+        value_coefficient=value_coefficient,
+        discount_factor=discount_factor,
+        gae_lambda=gae_lambda
+    )
diff --git a/vel/rl/algo/policy_gradient/acer.py b/vel/rl/policy/acer.py
similarity index 58%
rename from vel/rl/algo/policy_gradient/acer.py
rename to vel/rl/policy/acer.py
index 82cd3d9a..68792faf 100644
--- a/vel/rl/algo/policy_gradient/acer.py
+++ b/vel/rl/policy/acer.py
@@ -1,8 +1,13 @@
+import typing
+import gym
 import torch
 import torch.nn.functional as F
 
-from vel.api.metrics.averaging_metric import AveragingNamedMetric
-from vel.rl.api import Trajectories, OptimizerAlgoBase
+from vel.api import BackboneModule, ModuleFactory, BatchInfo, OptimizerFactory, VelOptimizer
+from vel.metric.base import AveragingNamedMetric
+from vel.rl.api import Trajectories, RlPolicy, Rollout
+from vel.rl.module.q_stochastic_policy import QStochasticPolicy
+from vel.util.situational import gym_space_to_size_hint
 
 
 def select_indices(tensor, indices):
@@ -10,18 +15,16 @@ def select_indices(tensor, indices):
     return tensor.gather(1, indices.unsqueeze(1)).squeeze()
 
 
-class AcerPolicyGradient(OptimizerAlgoBase):
+class ACER(RlPolicy):
     """ Actor-Critic with Experience Replay - policy gradient calculations """
 
-    def __init__(self, model_factory, discount_factor, trust_region: bool = True, entropy_coefficient: float = 0.01,
+    def __init__(self, net: BackboneModule, target_net: typing.Optional[BackboneModule], action_space: gym.Space,
+                 discount_factor: float, trust_region: bool = True, entropy_coefficient: float = 0.01,
                  q_coefficient: float = 0.5, rho_cap: float = 10.0, retrace_rho_cap: float = 1.0,
-                 max_grad_norm: float = None, average_model_alpha: float = 0.99, trust_region_delta: float = 1.0):
-        super().__init__(max_grad_norm)
-
-        self.discount_factor = discount_factor
+                 average_model_alpha: float = 0.99, trust_region_delta: float = 1.0):
+        super().__init__(discount_factor)
 
         self.trust_region = trust_region
-        self.model_factory = model_factory
 
         self.entropy_coefficient = entropy_coefficient
         self.q_coefficient = q_coefficient
@@ -30,39 +33,82 @@ def __init__(self, model_factory, discount_factor, trust_region: bool = True, en
         self.retrace_rho_cap = retrace_rho_cap
 
         # Trust region settings
-        self.average_model = None
         self.average_model_alpha = average_model_alpha
         self.trust_region_delta = trust_region_delta
 
-    def initialize(self, training_info, model, environment, device):
-        """ Initialize policy gradient from reinforcer settings """
+        self.net = QStochasticPolicy(net, action_space)
+
+        if self.trust_region:
+            self.target_net = QStochasticPolicy(target_net, action_space)
+            self.target_net.requires_grad_(False)
+        else:
+            self.target_net = None
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        parameters = filter(lambda p: p.requires_grad, self.net.parameters())
+        return optimizer_factory.instantiate(parameters)
+
+    def train(self, mode=True):
+        """ Override train to make sure target model is always in eval mode """
+        self.net.train(mode)
+
+        if self.trust_region:
+            self.target_net.train(False)
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+
         if self.trust_region:
-            self.average_model = self.model_factory.instantiate(action_space=environment.action_space).to(device)
-            self.average_model.load_state_dict(model.state_dict())
+            self.target_net.load_state_dict(self.net.state_dict())
 
-    def update_average_model(self, model):
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation)
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        logprobs, q = self(observation)
+        actions = self.net.action_head.sample(logprobs, deterministic=deterministic)
+
+        # log likelihood of selected action
+        action_logprobs = self.net.action_head.logprob(actions, logprobs)
+        values = (torch.exp(logprobs) * q).sum(dim=1)
+
+        return {
+            'actions': actions,
+            'q': q,
+            'values': values,
+            'action:logprobs': action_logprobs,
+            'logprobs': logprobs
+        }
+
+    def update_target_policy(self):
         """ Update weights of the average model with new model observation """
-        for model_param, average_param in zip(model.parameters(), self.average_model.parameters()):
+        for model_param, average_param in zip(self.net.parameters(), self.target_net.parameters()):
             # EWMA average model update
             average_param.data.mul_(self.average_model_alpha).add_(model_param.data * (1 - self.average_model_alpha))
 
-    def calculate_gradient(self, batch_info, device, model, rollout):
+    def post_optimization_step(self, batch_info: BatchInfo, rollout: Rollout):
+        """ Optional operations to perform after optimization """
+        # We calculate the trust-region update with respect to the average model
+        if self.trust_region:
+            self.update_target_policy()
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
         """ Calculate loss of the supplied rollout """
         assert isinstance(rollout, Trajectories), "ACER algorithm requires trajectory input"
 
         local_epsilon = 1e-6
 
-        evaluator = model.evaluate(rollout)
+        # Part 0.0 - Rollout values
+        actions = rollout.batch_tensor('actions')
+        rollout_probabilities = torch.exp(rollout.batch_tensor('logprobs'))
+        observations = rollout.batch_tensor('observations')
 
-        actions = evaluator.get('rollout:actions')
-        rollout_probabilities = torch.exp(evaluator.get('rollout:logprobs'))
-
-        # We calculate the trust-region update with respect to the average model
-        if self.trust_region:
-            self.update_average_model(model)
-
-        logprobs = evaluator.get('model:logprobs')
-        q = evaluator.get('model:q')
+        # PART 0.1 - Model evaluation
+        logprobs, q = self(observations)
 
         # Selected action values
         action_logprobs = select_indices(logprobs, actions)
@@ -90,7 +136,7 @@ def calculate_gradient(self, batch_info, device, model, rollout):
                 action_q.reshape(trajectory_rewards.size()),
                 model_state_values.reshape(trajectory_rewards.size()),
                 actions_rho.reshape(trajectory_rewards.size()),
-                rollout.rollout_tensors['final_values']
+                rollout.rollout_tensors['final.values']
             ).flatten()
 
             advantages = q_retraced - model_state_values
@@ -99,7 +145,7 @@ def calculate_gradient(self, batch_info, device, model, rollout):
             explained_variance = 1 - torch.var(q_retraced - action_q) / torch.var(q_retraced)
 
         # Entropy of the policy distribution
-        policy_entropy = torch.mean(model.entropy(logprobs))
+        policy_entropy = torch.mean(self.net.action_head.entropy(logprobs))
         policy_gradient_loss = -torch.mean(advantages * importance_sampling_coefficient * action_logprobs)
 
         # Policy gradient bias correction
@@ -121,8 +167,7 @@ def calculate_gradient(self, batch_info, device, model, rollout):
 
         if self.trust_region:
             with torch.no_grad():
-                average_evaluator = self.average_model.evaluate(rollout)
-                average_action_logits = average_evaluator.get('model:logprobs')
+                target_logprobs = self.target_net(observations)[0]
 
             actor_loss = policy_loss - self.entropy_coefficient * policy_entropy
             q_loss = self.q_coefficient * q_function_loss
@@ -134,7 +179,7 @@ def calculate_gradient(self, batch_info, device, model, rollout):
 
             # Analytically calculated derivative of KL divergence on logits
             # That makes it hardcoded for discrete action spaces
-            kl_divergence_grad_symbolic = - torch.exp(average_action_logits) / logprobs.size(0)
+            kl_divergence_grad_symbolic = - torch.exp(target_logprobs) / logprobs.size(0)
 
             k_dot_g = (actor_gradient * kl_divergence_grad_symbolic).sum(dim=-1)
             k_dot_k = (kl_divergence_grad_symbolic ** 2).sum(dim=-1)
@@ -195,7 +240,6 @@ def metrics(self) -> list:
             AveragingNamedMetric("policy_gradient_bias_correction"),
             AveragingNamedMetric("explained_variance"),
             AveragingNamedMetric("advantage_norm"),
-            AveragingNamedMetric("grad_norm"),
             AveragingNamedMetric("model_prob_std"),
             AveragingNamedMetric("rollout_prob_std"),
             AveragingNamedMetric("avg_q_selected"),
@@ -203,17 +247,61 @@ def metrics(self) -> list:
         ]
 
 
-def create(model, trust_region, entropy_coefficient, q_coefficient, max_grad_norm, discount_factor,
-           rho_cap=10.0, retrace_rho_cap=1.0, average_model_alpha=0.99, trust_region_delta=1.0):
+class ACERFactory(ModuleFactory):
+    """ Factory class for ACER policies """
+    def __init__(self, net_factory, trust_region: bool, entropy_coefficient: float, q_coefficient: float,
+                 discount_factor: float, rho_cap: float = 10.0, retrace_rho_cap: float = 1.0,
+                 average_model_alpha: float = 0.99, trust_region_delta: float = 1.0):
+        self.net_factory = net_factory
+        self.trust_region = trust_region
+        self.entropy_coefficient = entropy_coefficient
+        self.q_coefficient = q_coefficient
+        self.discount_factor = discount_factor
+        self.rho_cap = rho_cap
+        self.retrace_rho_cap = retrace_rho_cap
+        self.average_model_alpha = average_model_alpha
+        self.trust_region_delta = trust_region_delta
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+
+        if self.trust_region:
+            target_net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+        else:
+            target_net = None
+
+        return ACER(
+            net=net,
+            target_net=target_net,
+            action_space=action_space,
+            trust_region=self.trust_region,
+            entropy_coefficient=self.entropy_coefficient,
+            q_coefficient=self.q_coefficient,
+            discount_factor=self.discount_factor,
+            rho_cap=self.rho_cap,
+            retrace_rho_cap=self.retrace_rho_cap,
+            average_model_alpha=self.average_model_alpha,
+            trust_region_delta=self.trust_region_delta,
+        )
+
+
+def create(net, trust_region: bool, entropy_coefficient: float, q_coefficient: float, discount_factor: float,
+           rho_cap: float = 10.0, retrace_rho_cap: float = 1.0, average_model_alpha: float = 0.99,
+           trust_region_delta: float = 1.0):
     """ Vel factory function """
-    return AcerPolicyGradient(
+    return ACERFactory(
+        net_factory=net,
         trust_region=trust_region,
-        model_factory=model,
         entropy_coefficient=entropy_coefficient,
         q_coefficient=q_coefficient,
         rho_cap=rho_cap,
         retrace_rho_cap=retrace_rho_cap,
-        max_grad_norm=max_grad_norm,
         discount_factor=discount_factor,
         average_model_alpha=average_model_alpha,
         trust_region_delta=trust_region_delta
diff --git a/vel/rl/policy/ddpg.py b/vel/rl/policy/ddpg.py
new file mode 100644
index 00000000..84d64a60
--- /dev/null
+++ b/vel/rl/policy/ddpg.py
@@ -0,0 +1,193 @@
+import typing
+
+import gym
+import torch
+import torch.autograd
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vel.api import BackboneModule, BatchInfo, ModuleFactory, OptimizerFactory, VelOptimizer, SizeHints
+from vel.metric.base import AveragingNamedMetric
+from vel.rl.api import RlPolicy, Rollout
+from vel.rl.module.actor_critic_policy import ActorCriticPolicy
+from vel.rl.module.noise.ou_noise import OuNoise
+from vel.util.situational import gym_space_to_size_hint
+
+
+class DDPG(RlPolicy):
+    """ Deep Deterministic Policy Gradient (DDPG) - policy gradient calculations """
+
+    def __init__(self, net: BackboneModule, target_net: BackboneModule, action_space: gym.Space,
+                 discount_factor: float, tau: float, noise_std_dev: float):
+        super().__init__(discount_factor)
+
+        self.net = net
+        self.target_net = target_net
+
+        self.tau = tau
+        self.discount_factor = discount_factor
+
+        self.action_noise = OuNoise(std_dev=noise_std_dev, action_space=action_space)
+
+    def train(self, mode=True):
+        """ Override train to make sure target model is always in eval mode """
+        self.net.train(mode)
+        self.target_net.train(False)
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+        self.target_net.load_state_dict(self.net.state_dict())
+
+    def reset_episodic_state(self, dones: torch.Tensor):
+        """ Called by the rollout worker, whenever episode is finished """
+        self.action_noise.reset_episodic_state(dones)
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        return optimizer_factory.instantiate(self.net.grouped_parameters())
+
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation)
+
+    def act(self, observation, state=None, deterministic=False) -> dict:
+        """ Select actions based on model's output """
+        action, value = self(observation)
+
+        if deterministic:
+            noisy_action = action
+        else:
+            noisy_action = self.action_noise(action)
+
+        return {
+            'actions': noisy_action,
+            'values': value
+        }
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Calculate loss of the supplied rollout """
+        rollout = rollout.to_transitions()
+
+        dones = rollout.batch_tensor('dones')
+        rewards = rollout.batch_tensor('rewards')
+        observations_next = rollout.batch_tensor('observations_next')
+        actions = rollout.batch_tensor('actions')
+        observations = rollout.batch_tensor('observations')
+
+        # Calculate value loss - or critic loss
+        with torch.no_grad():
+            target_next_value = self.target_net.value(observations_next)
+            target_value = rewards + (1.0 - dones) * self.discount_factor * target_next_value
+
+        # Value estimation error vs the target network
+        model_value = self.net.value(observations, actions)
+        value_loss = F.mse_loss(model_value, target_value)
+
+        # It may seem a bit tricky what I'm doing here, but the underlying idea is simple
+        # All other implementations I found keep two separate optimizers for actor and critic
+        # and update them separately
+        # What I'm trying to do is to optimize them both with a single optimizer
+        # but I need to make sure gradients flow correctly
+        # From critic loss to critic network only and from actor loss to actor network only
+
+        # Backpropagate value loss to critic only
+        value_loss.backward()
+
+        model_action = self.net.action(observations)
+        model_action_value = self.net.value(observations, model_action)
+
+        policy_loss = -model_action_value.mean()
+
+        model_action_grad = torch.autograd.grad(policy_loss, model_action)[0]
+
+        # Backpropagate actor loss to actor only
+        model_action.backward(gradient=model_action_grad)
+
+        return {
+            'policy_loss': policy_loss.item(),
+            'value_loss': value_loss.item(),
+        }
+
+    def post_optimization_step(self, batch_info: BatchInfo, rollout: Rollout):
+        """ Steps to take after optimization has been done"""
+        # Update target model
+        for model_param, target_param in zip(self.net.parameters(), self.target_net.parameters()):
+            # EWMA average model update
+            target_param.data.mul_(1 - self.tau).add_(model_param.data * self.tau)
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("value_loss"),
+            AveragingNamedMetric("policy_loss"),
+        ]
+
+
+class DDPGFactory(ModuleFactory):
+    """ Factory for the DDPG policy """
+
+    def __init__(self, actor_net: ModuleFactory, critic_net: ModuleFactory,
+                 discount_factor: float, tau: float, noise_std_dev: float,
+                 input_net: typing.Optional[ModuleFactory] = None):
+        self.actor_net_factory = actor_net
+        self.critic_net_factory = critic_net
+        self.input_net_factory = input_net
+
+        self.discount_factor = discount_factor
+        self.tau = tau
+        self.noise_std_dev = noise_std_dev
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+        action_hint = gym_space_to_size_hint(action_space)
+
+        if self.input_net_factory is None:
+            target_input_net = input_net = nn.Identity()
+        else:
+            input_net = self.input_net_factory.instantiate(size_hint=size_hint, **extra_args)
+            target_input_net = self.input_net_factory.instantiate(size_hint=size_hint, **extra_args)
+            size_hint = input_net.size_hints()
+
+        critic_size_hint = SizeHints((size_hint.unwrap(), action_hint.unwrap()))
+
+        actor_net = self.actor_net_factory.instantiate(size_hint=size_hint, **extra_args)
+        critic_net = self.critic_net_factory.instantiate(size_hint=critic_size_hint, **extra_args)
+
+        net = ActorCriticPolicy(
+            input_net, actor_net, critic_net, action_space
+        )
+
+        target_actor_net = self.actor_net_factory.instantiate(size_hint=size_hint, **extra_args)
+        target_critic_net = self.critic_net_factory.instantiate(size_hint=critic_size_hint, **extra_args)
+
+        target_net = ActorCriticPolicy(
+            target_input_net, target_actor_net, target_critic_net, action_space
+        )
+
+        return DDPG(
+            net=net,
+            target_net=target_net,
+            action_space=action_space,
+            discount_factor=self.discount_factor,
+            tau=self.tau,
+            noise_std_dev=self.noise_std_dev
+        )
+
+
+def create(actor_net: ModuleFactory, critic_net: ModuleFactory,
+           discount_factor: float, tau: float, noise_std_dev: float,
+           input_net: typing.Optional[ModuleFactory] = None):
+    """ Vel factory function """
+    return DDPGFactory(
+        actor_net=actor_net,
+        critic_net=critic_net,
+        input_net=input_net,
+        discount_factor=discount_factor,
+        tau=tau,
+        noise_std_dev=noise_std_dev
+    )
diff --git a/vel/rl/policy/dqn.py b/vel/rl/policy/dqn.py
new file mode 100644
index 00000000..b6a27aa0
--- /dev/null
+++ b/vel/rl/policy/dqn.py
@@ -0,0 +1,179 @@
+import numbers
+
+import typing
+import gym
+import torch
+import torch.nn.functional as F
+import torch.nn.utils
+
+from vel.api import ModuleFactory, BackboneModule, BatchInfo, Schedule, OptimizerFactory, VelOptimizer
+from vel.function.constant import ConstantSchedule
+from vel.metric import AveragingNamedMetric
+from vel.rl.api import RlPolicy, Rollout
+from vel.rl.module.q_policy import QPolicy
+from vel.rl.module.noise.eps_greedy import EpsGreedy
+from vel.util.situational import gym_space_to_size_hint
+
+
+class DQN(RlPolicy):
+    """ Deep Q-Learning algorithm """
+
+    def __init__(self, net: BackboneModule, target_net: BackboneModule, action_space: gym.Space,
+                 epsilon: typing.Union[float, Schedule], discount_factor: float, double_dqn: bool,
+                 dueling_dqn: bool, target_update_frequency: int):
+        super().__init__(discount_factor)
+
+        self.net = QPolicy(net=net, action_space=action_space, dueling_dqn=dueling_dqn)
+        self.target_net = QPolicy(net=target_net, action_space=action_space, dueling_dqn=dueling_dqn)
+        self.target_net.requires_grad_(False)
+
+        self.double_dqn = double_dqn
+        self.target_update_frequency = target_update_frequency
+
+        if isinstance(epsilon, numbers.Number):
+            self.epsilon_schedule = ConstantSchedule(epsilon)
+        else:
+            self.epsilon_schedule = epsilon
+
+        self.epsilon_value = self.epsilon_schedule.value(0.0)
+        self.action_noise = EpsGreedy(action_space=action_space)
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        parameters = filter(lambda p: p.requires_grad, self.net.parameters())
+        return optimizer_factory.instantiate(parameters)
+
+    def train(self, mode=True):
+        """ Override train to make sure target model is always in eval mode """
+        self.net.train(mode)
+        self.target_net.train(False)
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+        self.target_net.load_state_dict(self.net.state_dict())
+
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation)
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        q_values = self.net(observation)
+        actions = self.net.q_head.sample(q_values)
+        noisy_actions = self.action_noise(actions, epsilon=self.epsilon_value, deterministic=deterministic)
+
+        return {
+            'actions': noisy_actions,
+            'q': q_values
+        }
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Calculate loss of the supplied rollout """
+        observations = rollout.batch_tensor('observations')
+        observations_next = rollout.batch_tensor('observations_next')
+
+        actions = rollout.batch_tensor('actions')
+        dones_tensor = rollout.batch_tensor('dones')
+        rewards_tensor = rollout.batch_tensor('rewards')
+
+        assert dones_tensor.dtype == torch.float32
+
+        q = self.net(observations)
+
+        with torch.no_grad():
+            target_q = self.target_net(observations_next)
+
+            if self.double_dqn:
+                # DOUBLE DQN
+                model_q_next = self.net(observations_next)
+                # Select largest 'target' value based on action that 'model' selects
+                values = target_q.gather(1, model_q_next.argmax(dim=1, keepdim=True)).squeeze(1)
+            else:
+                # REGULAR DQN
+                # [0] is because in pytorch .max(...) returns tuple (max values, argmax)
+                values = target_q.max(dim=1)[0]
+
+            forward_steps = rollout.extra_data.get('forward_steps', 1)
+            estimated_return = rewards_tensor + (self.discount_factor ** forward_steps) * values * (1 - dones_tensor)
+
+        q_selected = q.gather(1, actions.unsqueeze(1)).squeeze(1)
+
+        if rollout.has_tensor('weights'):
+            weights = rollout.batch_tensor('weights')
+        else:
+            weights = torch.ones_like(rewards_tensor)
+
+        original_losses = F.smooth_l1_loss(q_selected, estimated_return, reduction='none')
+
+        loss_value = torch.mean(weights * original_losses)
+        loss_value.backward()
+
+        return {
+            'loss': loss_value.item(),
+            # We need it to update priorities in the replay buffer:
+            'errors': original_losses.detach().cpu().numpy(),
+            'average_q_selected': torch.mean(q_selected).item(),
+            'average_q_target': torch.mean(estimated_return).item()
+        }
+
+    def post_optimization_step(self, batch_info, rollout):
+        """ Steps to take after optimization has been done"""
+        if batch_info.aggregate_batch_number % self.target_update_frequency == 0:
+            self.target_net.load_state_dict(self.net.state_dict())
+
+        self.epsilon_value = self.epsilon_schedule.value(batch_info['progress'])
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("loss", scope="model"),
+            AveragingNamedMetric("average_q_selected", scope="model"),
+            AveragingNamedMetric("average_q_target", scope="model")
+        ]
+
+
+class DQNFactory(ModuleFactory):
+    def __init__(self, net_factory: ModuleFactory, epsilon: typing.Union[float, Schedule], discount_factor: float,
+                 target_update_frequency: int, double_dqn: bool = False, dueling_dqn: bool = False):
+        self.net_factory = net_factory
+        self.epsilon = epsilon
+        self.discount_factor = discount_factor
+        self.target_update_frequency = target_update_frequency
+        self.double_dqn = double_dqn
+        self.dueling_dqn = dueling_dqn
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+        target_net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+
+        return DQN(
+            net=net,
+            target_net=target_net,
+            action_space=action_space,
+            epsilon=self.epsilon,
+            discount_factor=self.discount_factor,
+            double_dqn=self.double_dqn,
+            dueling_dqn=self.dueling_dqn,
+            target_update_frequency=self.target_update_frequency
+        )
+
+
+def create(net: ModuleFactory, epsilon: typing.Union[float, Schedule], discount_factor: float,
+           target_update_frequency: int, double_dqn: bool = False, dueling_dqn: bool = False):
+    """ Vel factory function """
+
+    return DQNFactory(
+        net_factory=net,
+        epsilon=epsilon,
+        discount_factor=discount_factor,
+        double_dqn=double_dqn,
+        dueling_dqn=dueling_dqn,
+        target_update_frequency=target_update_frequency,
+    )
diff --git a/vel/rl/policy/ppo.py b/vel/rl/policy/ppo.py
new file mode 100644
index 00000000..915a1bdf
--- /dev/null
+++ b/vel/rl/policy/ppo.py
@@ -0,0 +1,199 @@
+import gym
+import torch
+
+import numbers
+
+from vel.api import BatchInfo, ModuleFactory, BackboneModule
+from vel.util.situational import gym_space_to_size_hint
+from vel.util.stats import explained_variance
+from vel.function.constant import ConstantSchedule
+from vel.metric.base import AveragingNamedMetric
+
+from vel.rl.api import RlPolicy, Rollout, Trajectories
+from vel.rl.discount_bootstrap import discount_bootstrap_gae
+from vel.rl.module.stochastic_policy import StochasticPolicy
+
+
+class PPO(RlPolicy):
+    """ Proximal Policy Optimization - https://arxiv.org/abs/1707.06347 """
+    def __init__(self, net: BackboneModule, action_space: gym.Space,
+                 entropy_coefficient, value_coefficient, cliprange, discount_factor: float,
+                 normalize_advantage: bool = True, gae_lambda: float = 1.0):
+        super().__init__(discount_factor)
+
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.normalize_advantage = normalize_advantage
+        self.gae_lambda = gae_lambda
+
+        if isinstance(cliprange, numbers.Number):
+            self.cliprange = ConstantSchedule(cliprange)
+        else:
+            self.cliprange = cliprange
+
+        self.net = StochasticPolicy(net, action_space)
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation)
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        action_pd_params, value_output = self(observation)
+        actions = self.net.action_head.sample(action_pd_params, deterministic=deterministic)
+
+        # log likelihood of selected action
+        logprobs = self.net.action_head.logprob(actions, action_pd_params)
+
+        return {
+            'actions': actions,
+            'values': value_output,
+            'action:logprobs': logprobs
+        }
+
+    def process_rollout(self, rollout: Rollout):
+        """ Process rollout for optimization before any chunking/shuffling  """
+        assert isinstance(rollout, Trajectories), "PPO requires trajectory rollouts"
+
+        advantages = discount_bootstrap_gae(
+            rewards_buffer=rollout.transition_tensors['rewards'],
+            dones_buffer=rollout.transition_tensors['dones'],
+            values_buffer=rollout.transition_tensors['values'],
+            final_values=rollout.rollout_tensors['final.values'],
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda,
+            number_of_steps=rollout.num_steps
+        )
+
+        returns = advantages + rollout.transition_tensors['values']
+
+        rollout.transition_tensors['advantages'] = advantages
+        rollout.transition_tensors['returns'] = returns
+
+        return rollout
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Calculate loss of the supplied rollout """
+        observations = rollout.batch_tensor('observations')
+
+        # Part 0.0 - Rollout values
+        actions = rollout.batch_tensor('actions')
+        advantages = rollout.batch_tensor('advantages')
+        returns = rollout.batch_tensor('returns')
+        rollout_values = rollout.batch_tensor('values')
+
+        rollout_action_logprobs = rollout.batch_tensor('action:logprobs')
+
+        # PART 0.1 - Model evaluation
+        pd_params, model_values = self(observations)
+
+        model_action_logprobs = self.net.action_head.logprob(actions, pd_params)
+        entropy = self.net.action_head.entropy(pd_params)
+
+        # Select the cliprange
+        current_cliprange = self.cliprange.value(batch_info['progress'])
+
+        # Normalize the advantages?
+        if self.normalize_advantage:
+            advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+
+        # PART 1 - policy entropy
+        policy_entropy = torch.mean(entropy)
+
+        # PART 2 - value function
+        value_output_clipped = rollout_values + torch.clamp(
+            model_values - rollout_values, -current_cliprange, current_cliprange
+        )
+        value_loss_part1 = (model_values - returns).pow(2)
+        value_loss_part2 = (value_output_clipped - returns).pow(2)
+        value_loss = 0.5 * torch.mean(torch.max(value_loss_part1, value_loss_part2))
+
+        # PART 3 - policy gradient loss
+        ratio = torch.exp(model_action_logprobs - rollout_action_logprobs)
+
+        pg_loss_part1 = -advantages * ratio
+        pg_loss_part2 = -advantages * torch.clamp(ratio, 1.0 - current_cliprange, 1.0 + current_cliprange)
+        policy_loss = torch.mean(torch.max(pg_loss_part1, pg_loss_part2))
+
+        loss_value = (
+            policy_loss - self.entropy_coefficient * policy_entropy + self.value_coefficient * value_loss
+        )
+
+        loss_value.backward()
+
+        with torch.no_grad():
+            approx_kl_divergence = 0.5 * torch.mean((model_action_logprobs - rollout_action_logprobs).pow(2))
+            clip_fraction = torch.mean((torch.abs(ratio - 1.0) > current_cliprange).to(dtype=torch.float))
+
+        return {
+            'policy_loss': policy_loss.item(),
+            'value_loss': value_loss.item(),
+            'policy_entropy': policy_entropy.item(),
+            'approx_kl_divergence': approx_kl_divergence.item(),
+            'clip_fraction': clip_fraction.item(),
+            'advantage_norm': torch.norm(advantages).item(),
+            'explained_variance': explained_variance(returns, rollout_values)
+        }
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("policy_loss", scope="model"),
+            AveragingNamedMetric("value_loss", scope="model"),
+            AveragingNamedMetric("policy_entropy", scope="model"),
+            AveragingNamedMetric("approx_kl_divergence", scope="model"),
+            AveragingNamedMetric("clip_fraction", scope="model"),
+            AveragingNamedMetric("advantage_norm", scope="model"),
+            AveragingNamedMetric("explained_variance", scope="model")
+        ]
+
+
+class PPOFactory(ModuleFactory):
+    """ Factory class for policy gradient models """
+    def __init__(self, net_factory, entropy_coefficient, value_coefficient, cliprange, discount_factor: float,
+                 normalize_advantage: bool = True, gae_lambda: float = 1.0):
+        self.net_factory = net_factory
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.cliprange = cliprange
+        self.discount_factor = discount_factor
+        self.normalize_advantage = normalize_advantage
+        self.gae_lambda = gae_lambda
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+
+        return PPO(
+            net=net,
+            action_space=action_space,
+            entropy_coefficient=self.entropy_coefficient,
+            value_coefficient=self.value_coefficient,
+            cliprange=self.cliprange,
+            discount_factor=self.discount_factor,
+            normalize_advantage=self.normalize_advantage,
+            gae_lambda=self.gae_lambda,
+        )
+
+
+def create(net: ModuleFactory, entropy_coefficient, value_coefficient, cliprange, discount_factor: float,
+           normalize_advantage: bool = True, gae_lambda: float = 1.0):
+    """ Vel factory function """
+    return PPOFactory(
+        net_factory=net,
+        entropy_coefficient=entropy_coefficient,
+        value_coefficient=value_coefficient,
+        cliprange=cliprange,
+        discount_factor=discount_factor,
+        normalize_advantage=normalize_advantage,
+        gae_lambda=gae_lambda
+    )
diff --git a/vel/rl/policy/ppo_rnn.py b/vel/rl/policy/ppo_rnn.py
new file mode 100644
index 00000000..c09e08cc
--- /dev/null
+++ b/vel/rl/policy/ppo_rnn.py
@@ -0,0 +1,239 @@
+import numbers
+
+import gym
+import torch
+
+from vel.api import BatchInfo, ModuleFactory, BackboneModule
+from vel.function.constant import ConstantSchedule
+from vel.metric.base import AveragingNamedMetric
+from vel.rl.api import RlPolicy, Rollout, Trajectories
+from vel.rl.discount_bootstrap import discount_bootstrap_gae
+from vel.rl.module.stochastic_rnn_policy import StochasticRnnPolicy
+from vel.util.situational import gym_space_to_size_hint
+from vel.util.stats import explained_variance
+
+
+class PPORnn(RlPolicy):
+    """ Proximal Policy Optimization - https://arxiv.org/abs/1707.06347 """
+    def __init__(self, net: BackboneModule, action_space: gym.Space,
+                 entropy_coefficient, value_coefficient, cliprange, discount_factor: float,
+                 normalize_advantage: bool = True, gae_lambda: float = 1.0):
+        super().__init__(discount_factor)
+
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.normalize_advantage = normalize_advantage
+        self.gae_lambda = gae_lambda
+
+        if isinstance(cliprange, numbers.Number):
+            self.cliprange = ConstantSchedule(cliprange)
+        else:
+            self.cliprange = cliprange
+
+        self.net = StochasticRnnPolicy(net, action_space)
+
+        assert self.net.is_stateful, "Policy must be stateful"
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation, state=state)
+
+    def is_stateful(self) -> bool:
+        return self.net.is_stateful
+
+    def zero_state(self, batch_size):
+        return self.net.zero_state(batch_size)
+
+    def reset_state(self, state, dones):
+        return self.net.reset_state(state, dones)
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        action_pd_params, value_output, next_state = self(observation, state=state)
+        actions = self.net.action_head.sample(action_pd_params, deterministic=deterministic)
+
+        # log likelihood of selected action
+        logprobs = self.net.action_head.logprob(actions, action_pd_params)
+
+        return {
+            'action:logprobs': logprobs,
+            'actions': actions,
+            'state': next_state,
+            'values': value_output,
+        }
+
+    def process_rollout(self, rollout: Rollout) -> Rollout:
+        """ Process rollout for optimization before any chunking/shuffling  """
+        assert isinstance(rollout, Trajectories), "PPO requires trajectory rollouts"
+
+        advantages = discount_bootstrap_gae(
+            rewards_buffer=rollout.transition_tensors['rewards'],
+            dones_buffer=rollout.transition_tensors['dones'],
+            values_buffer=rollout.transition_tensors['values'],
+            final_values=rollout.rollout_tensors['final.values'],
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda,
+            number_of_steps=rollout.num_steps
+        )
+
+        returns = advantages + rollout.transition_tensors['values']
+
+        rollout.transition_tensors['advantages'] = advantages
+        rollout.transition_tensors['returns'] = returns
+
+        return rollout
+
+    def _extract_initial_state(self, transition_tensors):
+        """ Extract initial state from the state dictionary """
+        state = {}
+
+        idx = len('state') + 1
+
+        for key, value in transition_tensors.items():
+            if key.startswith('state'):
+                state[key[idx:]] = value[0]
+
+        return state
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Calculate loss of the supplied rollout """
+        assert isinstance(rollout, Trajectories), "For an RNN model, we must evaluate trajectories"
+
+        # Part 0.0 - Rollout values
+        actions = rollout.batch_tensor('actions')
+        advantages = rollout.batch_tensor('advantages')
+        returns = rollout.batch_tensor('returns')
+        rollout_values = rollout.batch_tensor('values')
+        rollout_action_logprobs = rollout.batch_tensor('action:logprobs')
+
+        # PART 0.1 - Model evaluation
+        observations = rollout.transition_tensors['observations']
+        hidden_state = self._extract_initial_state(rollout.transition_tensors)
+        dones = rollout.transition_tensors['dones']
+
+        action_accumulator = []
+        value_accumulator = []
+
+        # Evaluate recurrent network step by step
+        for i in range(observations.size(0)):
+            action_output, value_output, hidden_state = self(observations[i], hidden_state)
+            hidden_state = self.reset_state(hidden_state, dones[i])
+
+            action_accumulator.append(action_output)
+            value_accumulator.append(value_output)
+
+        pd_params = torch.cat(action_accumulator, dim=0)
+        model_values = torch.cat(value_accumulator, dim=0)
+
+        model_action_logprobs = self.net.action_head.logprob(actions, pd_params)
+        entropy = self.net.action_head.entropy(pd_params)
+
+        # Select the cliprange
+        current_cliprange = self.cliprange.value(batch_info['progress'])
+
+        # Normalize the advantages?
+        if self.normalize_advantage:
+            advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+
+        # PART 1 - policy entropy
+        policy_entropy = torch.mean(entropy)
+
+        # PART 2 - value function
+        value_output_clipped = rollout_values + torch.clamp(
+            model_values - rollout_values, -current_cliprange, current_cliprange
+        )
+        value_loss_part1 = (model_values - returns).pow(2)
+        value_loss_part2 = (value_output_clipped - returns).pow(2)
+        value_loss = 0.5 * torch.mean(torch.max(value_loss_part1, value_loss_part2))
+
+        # PART 3 - policy gradient loss
+        ratio = torch.exp(model_action_logprobs - rollout_action_logprobs)
+
+        pg_loss_part1 = -advantages * ratio
+        pg_loss_part2 = -advantages * torch.clamp(ratio, 1.0 - current_cliprange, 1.0 + current_cliprange)
+        policy_loss = torch.mean(torch.max(pg_loss_part1, pg_loss_part2))
+
+        loss_value = (
+            policy_loss - self.entropy_coefficient * policy_entropy + self.value_coefficient * value_loss
+        )
+
+        loss_value.backward()
+
+        with torch.no_grad():
+            approx_kl_divergence = 0.5 * torch.mean((model_action_logprobs - rollout_action_logprobs).pow(2))
+            clip_fraction = torch.mean((torch.abs(ratio - 1.0) > current_cliprange).to(dtype=torch.float))
+
+        return {
+            'policy_loss': policy_loss.item(),
+            'value_loss': value_loss.item(),
+            'policy_entropy': policy_entropy.item(),
+            'approx_kl_divergence': approx_kl_divergence.item(),
+            'clip_fraction': clip_fraction.item(),
+            'advantage_norm': torch.norm(advantages).item(),
+            'explained_variance': explained_variance(returns, rollout_values)
+        }
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("policy_loss", scope="model"),
+            AveragingNamedMetric("value_loss", scope="model"),
+            AveragingNamedMetric("policy_entropy", scope="model"),
+            AveragingNamedMetric("approx_kl_divergence", scope="model"),
+            AveragingNamedMetric("clip_fraction", scope="model"),
+            AveragingNamedMetric("advantage_norm", scope="model"),
+            AveragingNamedMetric("explained_variance", scope="model")
+        ]
+
+
+class PPORnnFactory(ModuleFactory):
+    """ Factory class for policy gradient models """
+    def __init__(self, net_factory,
+                 entropy_coefficient, value_coefficient, cliprange, discount_factor: float,
+                 normalize_advantage: bool = True, gae_lambda: float = 1.0):
+        self.net_factory = net_factory
+        self.entropy_coefficient = entropy_coefficient
+        self.value_coefficient = value_coefficient
+        self.cliprange = cliprange
+        self.discount_factor = discount_factor
+        self.normalize_advantage = normalize_advantage
+        self.gae_lambda = gae_lambda
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+
+        return PPORnn(
+            net=net,
+            entropy_coefficient=self.entropy_coefficient,
+            action_space=action_space,
+            value_coefficient=self.value_coefficient,
+            cliprange=self.cliprange,
+            discount_factor=self.discount_factor,
+            normalize_advantage=self.normalize_advantage,
+            gae_lambda=self.gae_lambda,
+        )
+
+
+def create(net: ModuleFactory,
+           entropy_coefficient, value_coefficient, cliprange, discount_factor: float,
+           normalize_advantage: bool = True, gae_lambda: float = 1.0):
+    """ Vel factory function """
+    return PPORnnFactory(
+        net_factory=net,
+        entropy_coefficient=entropy_coefficient,
+        value_coefficient=value_coefficient,
+        cliprange=cliprange,
+        discount_factor=discount_factor,
+        normalize_advantage=normalize_advantage,
+        gae_lambda=gae_lambda
+    )
diff --git a/vel/rl/policy/rainbow.py b/vel/rl/policy/rainbow.py
new file mode 100644
index 00000000..be7253c9
--- /dev/null
+++ b/vel/rl/policy/rainbow.py
@@ -0,0 +1,255 @@
+import gym
+import torch
+import torch.nn.utils
+
+from vel.api import ModuleFactory, BackboneModule, BatchInfo, OptimizerFactory, VelOptimizer
+from vel.metric import AveragingNamedMetric
+from vel.rl.api import RlPolicy, Rollout
+from vel.rl.module.rainbow_policy import RainbowPolicy
+from vel.util.situational import gym_space_to_size_hint
+
+
+class Rainbow(RlPolicy):
+    """ Deep Q-Learning algorithm """
+
+    def __init__(self, net: BackboneModule, target_net: BackboneModule, action_space: gym.Space,
+                 discount_factor: float, target_update_frequency: int, vmin: float, vmax: float, atoms: int = 1,
+                 initial_std_dev: float = 0.4, factorized_noise: bool = True):
+        super().__init__(discount_factor)
+
+        self.net = RainbowPolicy(
+            net=net,
+            action_space=action_space,
+            vmin=vmin,
+            vmax=vmax,
+            atoms=atoms,
+            initial_std_dev=initial_std_dev,
+            factorized_noise=factorized_noise
+        )
+
+        self.target_net = RainbowPolicy(
+            net=target_net,
+            action_space=action_space,
+            vmin=vmin,
+            vmax=vmax,
+            atoms=atoms,
+            initial_std_dev=initial_std_dev,
+            factorized_noise=factorized_noise
+        )
+        self.target_net.requires_grad_(False)
+
+        self.discount_factor = discount_factor
+        self.target_update_frequency = target_update_frequency
+
+        self.vmin = vmin
+        self.vmax = vmax
+        self.num_atoms = atoms
+
+        # self.support_atoms = self.model.q
+        # self.atom_delta = histogram_info['atom_delta']
+        self.register_buffer('support_atoms', self.net.support_atoms.clone())
+        self.atom_delta = self.net.atom_delta
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        parameters = filter(lambda p: p.requires_grad, self.net.parameters())
+        return optimizer_factory.instantiate(parameters)
+
+    def train(self, mode=True):
+        """ Override train to make sure target model is always in eval mode """
+        self.net.train(mode)
+        self.target_net.train(False)
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        self.net.reset_weights()
+        self.target_net.load_state_dict(self.net.state_dict())
+
+    def forward(self, observation, state=None):
+        """ Calculate model outputs """
+        return self.net(observation)
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        self.train(mode=not deterministic)
+
+        q_values = self.net(observation)
+        actions = self.net.q_head.sample(q_values)
+
+        return {
+            'actions': actions,
+            'q': q_values
+        }
+
+    def calculate_gradient(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Calculate loss of the supplied rollout """
+        batch_size = rollout.frames()
+
+        observations = rollout.batch_tensor('observations')
+        observations_next = rollout.batch_tensor('observations_next')
+
+        actions = rollout.batch_tensor('actions')
+        dones_tensor = rollout.batch_tensor('dones')
+        rewards_tensor = rollout.batch_tensor('rewards')
+
+        assert dones_tensor.dtype == torch.float32
+
+        q = self.net(observations)
+
+        with torch.no_grad():
+            # DOUBLE DQN
+            # Histogram gets returned as logits initially, we need to exp it before projection
+            target_value_histogram_for_all_actions = self.target_net(observations_next).exp()
+            model_value_histogram_for_all_actions = self.net(observations_next).exp()
+
+            atoms_aligned = self.support_atoms.view(1, 1, self.num_atoms)
+
+            selected_action_indices = (
+                (atoms_aligned * model_value_histogram_for_all_actions).sum(dim=-1).argmax(dim=1)
+            )
+
+            # Select largest 'target' value based on action that 'model' selects
+            next_value_histograms = (
+                target_value_histogram_for_all_actions[range(batch_size), selected_action_indices]
+            )
+
+            # HISTOGRAM PROJECTION CODE
+            forward_steps = rollout.extra_data.get('forward_steps', 1)
+
+            atoms_projected = (
+                rewards_tensor.unsqueeze(1) +
+                (self.discount_factor ** forward_steps) *
+                (1 - dones_tensor).unsqueeze(1) * self.support_atoms.unsqueeze(0)
+            )
+
+            atoms_projected = atoms_projected.clamp(min=self.vmin, max=self.vmax)
+            projection_indices = (atoms_projected - self.vmin) / self.atom_delta
+
+            index_floor = projection_indices.floor().long()
+            index_ceil = projection_indices.ceil().long()
+
+            # Fix corner case when index_floor == index_ceil
+            index_floor[(index_ceil > 0) * (index_floor == index_ceil)] -= 1
+            index_ceil[(index_floor < (self.num_atoms - 1)) * (index_floor == index_ceil)] += 1
+
+            value_histogram_projected = torch.zeros_like(next_value_histograms)
+
+            # Following part will be a bit convoluted, in an effort to fully vectorize projection operation
+
+            # Special offset index tensor
+            offsets = (
+                torch.arange(0, batch_size * self.num_atoms, self.num_atoms)
+                .unsqueeze(1)
+                .expand(batch_size, self.num_atoms)
+                .contiguous().view(-1).to(value_histogram_projected.device)
+            )
+
+            # Linearize all the buffers
+            value_histogram_projected = value_histogram_projected.view(-1)
+            index_ceil = index_ceil.view(-1)
+            index_floor = index_floor.view(-1)
+            projection_indices = projection_indices.view(-1)
+
+            value_histogram_projected.index_add_(
+                0,
+                index_floor+offsets,
+                (next_value_histograms.view(-1) * (index_ceil.float() - projection_indices))
+            )
+
+            value_histogram_projected.index_add_(
+                0,
+                index_ceil+offsets,
+                (next_value_histograms.view(-1) * (projection_indices - index_floor.float()))
+            )
+
+            value_histogram_projected = value_histogram_projected.reshape(next_value_histograms.shape)
+
+        q_log_histogram_selected = q[range(q.size(0)), actions]
+
+        # Cross-entropy loss as usual
+        original_losses = -(value_histogram_projected * q_log_histogram_selected).sum(dim=1)
+
+        if rollout.has_tensor('weights'):
+            weights = rollout.batch_tensor('weights')
+        else:
+            weights = torch.ones_like(rewards_tensor)
+
+        loss_value = torch.mean(weights * original_losses)
+        loss_value.backward()
+
+        with torch.no_grad():
+            mean_q_model = (self.support_atoms.unsqueeze(0) * torch.exp(q_log_histogram_selected)).sum(dim=1).mean()
+            mean_q_target = (self.support_atoms.unsqueeze(0) * value_histogram_projected).sum(dim=1).mean()
+
+        return {
+            'loss': loss_value.item(),
+            # We need it to update priorities in the replay buffer:
+            'errors': original_losses.detach().cpu().numpy(),
+            'average_q_selected': mean_q_model.item(),
+            'average_q_target': mean_q_target.item()
+        }
+
+    def post_optimization_step(self, batch_info, rollout):
+        """ Steps to take after optimization has been done"""
+        if batch_info.aggregate_batch_number % self.target_update_frequency == 0:
+            self.target_net.load_state_dict(self.net.state_dict())
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("loss"),
+            AveragingNamedMetric("average_q_selected"),
+            AveragingNamedMetric("average_q_target")
+        ]
+
+
+class RainbowFactory(ModuleFactory):
+    def __init__(self, net_factory: ModuleFactory, discount_factor: float, target_update_frequency: int,
+                 vmin: float, vmax: float, atoms: int = 1, initial_std_dev: float = 0.4, factorized_noise: bool = True):
+        self.net_factory = net_factory
+        self.discount_factor = discount_factor
+        self.target_update_frequency = target_update_frequency
+        self.vmin = vmin
+        self.vmax = vmax
+        self.atoms = atoms
+        self.initial_std_dev = initial_std_dev
+        self.factorized_noise = factorized_noise
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        # TODO(jerry): Push noisy net parameters down the stack here
+        net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+        target_net = self.net_factory.instantiate(size_hint=size_hint, **extra_args)
+
+        return Rainbow(
+            net=net,
+            target_net=target_net,
+            action_space=action_space,
+            discount_factor=self.discount_factor,
+            target_update_frequency=self.target_update_frequency,
+            vmin=self.vmin,
+            vmax=self.vmax,
+            atoms=self.atoms,
+            initial_std_dev=self.initial_std_dev,
+            factorized_noise=self.factorized_noise
+        )
+
+
+def create(net: ModuleFactory, discount_factor: float, target_update_frequency: int,
+           vmin: float, vmax: float, atoms: int = 1, initial_std_dev: float = 0.4, factorized_noise: bool = True):
+    """ Vel factory function """
+    return RainbowFactory(
+        net_factory=net,
+        discount_factor=discount_factor,
+        target_update_frequency=target_update_frequency,
+        vmin=vmin,
+        vmax=vmax,
+        atoms=atoms,
+        initial_std_dev=initial_std_dev,
+        factorized_noise=factorized_noise
+    )
diff --git a/vel/rl/policy/trpo.py b/vel/rl/policy/trpo.py
new file mode 100644
index 00000000..58c44ca6
--- /dev/null
+++ b/vel/rl/policy/trpo.py
@@ -0,0 +1,425 @@
+import gym
+import numpy as np
+import itertools as it
+import typing
+
+import torch
+import torch.autograd as autograd
+import torch.nn.functional as F
+import torch.nn.utils
+
+from vel.api import BatchInfo, VelOptimizer, OptimizerFactory, ModuleFactory, BackboneModule
+from vel.util.stats import explained_variance
+from vel.metric.base import AveragingNamedMetric
+
+from vel.rl.api import Rollout, Trajectories, RlPolicy
+from vel.rl.discount_bootstrap import discount_bootstrap_gae
+from vel.rl.module.head.stochastic_action_head import make_stockastic_action_head
+from vel.rl.module.head.value_head import ValueHead
+from vel.util.situational import gym_space_to_size_hint
+
+
+def p2v(params):
+    """ Parameters to vector - shorthand utility version """
+    return torch.nn.utils.parameters_to_vector(params)
+
+
+def v2p(vector, params):
+    """ Vector to parameters - shorthand utility version """
+    return torch.nn.utils.vector_to_parameters(vector, params)
+
+
+def conjugate_gradient_method(matrix_vector_operator, loss_gradient, nsteps, rdotr_tol=1e-10):
+    """ Conjugate gradient algorithm """
+    x = torch.zeros_like(loss_gradient)
+
+    r = loss_gradient.clone()
+    p = loss_gradient.clone()
+
+    rdotr = torch.dot(r, r)
+
+    for i in range(nsteps):
+        avp = matrix_vector_operator(p)
+        alpha = rdotr / torch.dot(p, avp)
+
+        x += alpha * p
+        r -= alpha * avp
+
+        new_rdotr = torch.dot(r, r)
+        betta = new_rdotr / rdotr
+        p = r + betta * p
+        rdotr = new_rdotr
+
+        if rdotr < rdotr_tol:
+            break
+
+    return x
+
+
+class TRPO(RlPolicy):
+    """ Trust Region Policy Optimization - https://arxiv.org/abs/1502.05477 """
+
+    def __init__(self, policy_net: BackboneModule, value_net: BackboneModule, action_space: gym.Space,
+                 max_kl, cg_iters, line_search_iters, cg_damping, entropy_coefficient, vf_iters,
+                 discount_factor, gae_lambda, improvement_acceptance_ratio,
+                 input_net: typing.Optional[BackboneModule] = None,
+                 ):
+        super().__init__(discount_factor)
+
+        self.mak_kl = max_kl
+        self.cg_iters = cg_iters
+        self.line_search_iters = line_search_iters
+        self.cg_damping = cg_damping
+        self.entropy_coefficient = entropy_coefficient
+        self.vf_iters = vf_iters
+        self.gae_lambda = gae_lambda
+        self.improvement_acceptance_ratio = improvement_acceptance_ratio
+
+        self.input_net = input_net
+        self.policy_net = policy_net
+        self.value_net = value_net
+
+        self.action_head = make_stockastic_action_head(
+            action_space=action_space,
+            input_dim=self.policy_net.size_hints().assert_single(2).last()
+        )
+
+        self.value_head = ValueHead(
+            input_dim=self.value_net.size_hints().assert_single(2).last()
+        )
+
+    def reset_weights(self):
+        """ Initialize properly model weights """
+        if self.input_net:
+            self.input_net.reset_weights()
+
+        self.policy_net.reset_weights()
+        self.value_net.reset_weights()
+
+        self.action_head.reset_weights()
+        self.value_head.reset_weights()
+
+    def forward(self, observations):
+        """ Calculate model outputs """
+        if self.input_net is not None:
+            normalized_observations = self.input_net(observations)
+        else:
+            normalized_observations = observations
+
+        policy_base_output = self.policy_net(normalized_observations)
+        value_base_output = self.value_net(normalized_observations)
+
+        action_output = self.action_head(policy_base_output)
+        value_output = self.value_head(value_base_output)
+
+        return action_output, value_output
+
+    def _value(self, normalized_observations):
+        """ Calculate only value head for given state """
+        base_output = self.value_net(normalized_observations)
+        value_output = self.value_head(base_output)
+        return value_output
+
+    def _policy(self, normalized_observations):
+        """ Calculate only action head for given state """
+        policy_base_output = self.policy_net(normalized_observations)
+        policy_params = self.action_head(policy_base_output)
+        return policy_params
+
+    def act(self, observation, state=None, deterministic=False):
+        """ Select actions based on model's output """
+        action_pd_params, value_output = self(observation)
+        actions = self.action_head.sample(action_pd_params, deterministic=deterministic)
+
+        # log likelihood of selected action
+        logprobs = self.action_head.logprob(actions, action_pd_params)
+
+        return {
+            'actions': actions,
+            'values': value_output,
+            'action:logprobs': logprobs
+        }
+
+    def create_optimizer(self, optimizer_factory: OptimizerFactory) -> VelOptimizer:
+        """ Create optimizer for the purpose of optimizing this model """
+        parameters = filter(lambda p: p.requires_grad, self.value_parameters())
+        return optimizer_factory.instantiate(parameters)
+
+    def policy_parameters(self):
+        """ Parameters of policy """
+        return it.chain(
+            self.policy_net.parameters(),
+            self.action_head.parameters()
+        )
+
+    def value_parameters(self):
+        """ Parameters of value function """
+        return it.chain(
+            self.value_net.parameters(),
+            self.value_head.parameters()
+        )
+
+    def process_rollout(self, rollout: Rollout):
+        """ Process rollout for optimization before any chunking/shuffling  """
+        assert isinstance(rollout, Trajectories), "PPO requires trajectory rollouts"
+
+        advantages = discount_bootstrap_gae(
+            rewards_buffer=rollout.transition_tensors['rewards'],
+            dones_buffer=rollout.transition_tensors['dones'],
+            values_buffer=rollout.transition_tensors['values'],
+            final_values=rollout.rollout_tensors['final.values'],
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda,
+            number_of_steps=rollout.num_steps
+        )
+
+        returns = advantages + rollout.transition_tensors['values']
+
+        rollout.transition_tensors['advantages'] = advantages
+        rollout.transition_tensors['returns'] = returns
+
+        return rollout
+
+    def optimize(self, batch_info: BatchInfo, rollout: Rollout) -> dict:
+        """ Single optimization step for a model """
+        rollout = rollout.to_transitions()
+
+        observations = rollout.batch_tensor('observations')
+
+        if self.input_net is not None:
+            normalized_observations = self.input_net(observations)
+        else:
+            normalized_observations = observations
+
+        returns = rollout.batch_tensor('returns')
+
+        # Evaluate model on the observations
+        action_pd_params = self._policy(normalized_observations)
+        policy_entropy = torch.mean(self.action_head.entropy(action_pd_params))
+
+        policy_loss = self.calc_policy_loss(action_pd_params, policy_entropy, rollout)
+        policy_grad = p2v(autograd.grad(policy_loss, self.policy_parameters(), retain_graph=True)).detach()
+
+        # Calculate gradient of KL divergence of model with fixed version of itself
+        # Value of kl_divergence will be 0, but what we need is the gradient, actually the 2nd derivarive
+        kl_divergence = torch.mean(self.action_head.kl_divergence(action_pd_params.detach(), action_pd_params))
+        kl_divergence_gradient = p2v(torch.autograd.grad(kl_divergence, self.policy_parameters(), create_graph=True))
+
+        step_direction = conjugate_gradient_method(
+            matrix_vector_operator=lambda x: self.fisher_vector_product(x, kl_divergence_gradient),
+            # Because we want to decrease the loss, we want to go into the direction of -gradient
+            loss_gradient=-policy_grad,
+            nsteps=self.cg_iters
+        )
+
+        shs = 0.5 * step_direction @ self.fisher_vector_product(step_direction, kl_divergence_gradient)
+        lm = torch.sqrt(shs / self.mak_kl)
+        full_step = step_direction / lm
+
+        # Because we want to decrease the loss, we want to go into the direction of -gradient
+        expected_improvement = (-policy_grad) @ full_step
+        original_parameter_vec = p2v(self.policy_parameters()).detach_()
+
+        (policy_optimization_success, ratio, policy_loss_improvement, new_policy_loss, kl_divergence_step) = (
+            self.line_search(
+                normalized_observations, rollout, policy_loss, action_pd_params, original_parameter_vec,
+                full_step, expected_improvement
+            )
+        )
+
+        gradient_norms = []
+
+        for i in range(self.vf_iters):
+            batch_info.optimizer.zero_grad()
+            value_loss = self._value_loss(normalized_observations, returns)
+
+            value_loss.backward()
+
+            batch_info.optimizer.step(closure=None)
+
+        if gradient_norms:
+            gradient_norm = np.mean(gradient_norms)
+        else:
+            gradient_norm = 0.0
+
+        # noinspection PyUnboundLocalVariable
+        return {
+            'new_policy_loss': new_policy_loss.item(),
+            'policy_entropy': policy_entropy.item(),
+            'value_loss': value_loss.item(),
+            'policy_optimization_success': float(policy_optimization_success),
+            'policy_improvement_ratio': ratio.item(),
+            'kl_divergence_step': kl_divergence_step.item(),
+            'policy_loss_improvement': policy_loss_improvement.item(),
+            'grad_norm': gradient_norm,
+            'advantage_norm': torch.norm(rollout.batch_tensor('advantages')).item(),
+            'explained_variance': explained_variance(returns, rollout.batch_tensor('values'))
+        }
+
+    def line_search(self, normalized_observations, rollout, original_policy_loss, original_policy_params,
+                    original_parameter_vec, full_step, expected_improvement_full):
+        """ Find the right stepsize to make sure policy improves """
+        current_parameter_vec = original_parameter_vec.clone()
+
+        for idx in range(self.line_search_iters):
+            stepsize = 0.5 ** idx
+
+            new_parameter_vec = current_parameter_vec + stepsize * full_step
+
+            # Update model parameters
+            v2p(new_parameter_vec, self.policy_parameters())
+
+            # Calculate new loss
+            with torch.no_grad():
+                policy_params = self._policy(normalized_observations)
+                policy_entropy = torch.mean(self.action_head.entropy(policy_params))
+                kl_divergence = torch.mean(self.action_head.kl_divergence(original_policy_params, policy_params))
+
+                new_loss = self.calc_policy_loss(policy_params, policy_entropy, rollout)
+
+                actual_improvement = original_policy_loss - new_loss
+                expected_improvement = expected_improvement_full * stepsize
+
+                ratio = actual_improvement / expected_improvement
+
+            if kl_divergence.item() > self.mak_kl * 1.5:
+                # KL divergence bound exceeded
+                continue
+            elif ratio < expected_improvement:
+                # Not enough loss improvement
+                continue
+            else:
+                # Optimization successful
+                return True, ratio, actual_improvement, new_loss, kl_divergence
+
+        # Optimization failed, revert to initial parameters
+        v2p(original_parameter_vec, self.policy_parameters())
+        return False, torch.tensor(0.0), torch.tensor(0.0), torch.tensor(0.0), torch.tensor(0.0)
+
+    def fisher_vector_product(self, vector, kl_divergence_gradient):
+        """ Calculate product Hessian @ vector """
+        assert not vector.requires_grad, "Vector must not propagate gradient"
+        dot_product = vector @ kl_divergence_gradient
+
+        # at least one dimension spans across two contiguous subspaces
+        double_gradient = torch.autograd.grad(dot_product, self.policy_parameters(), retain_graph=True)
+        fvp = p2v(x.contiguous() for x in double_gradient)
+
+        return fvp + vector * self.cg_damping
+
+    def _value_loss(self, normalized_observations, returns):
+        """ Loss of value function head """
+        value_outputs = self._value(normalized_observations)
+        value_loss = 0.5 * F.mse_loss(value_outputs, returns)
+        return value_loss
+
+    def calc_policy_loss(self, policy_params, policy_entropy, rollout):
+        """
+        Policy gradient loss - calculate from probability distribution
+
+        Calculate surrogate loss - advantage * policy_probability / fixed_initial_policy_probability
+
+        Because we operate with logarithm of -probability (neglogp) we do
+        - advantage * exp(fixed_neglogps - model_neglogps)
+        """
+        actions = rollout.batch_tensor('actions')
+        advantages = rollout.batch_tensor('advantages')
+        fixed_logprobs = rollout.batch_tensor('action:logprobs')
+
+        model_logprobs = self.action_head.logprob(actions, policy_params)
+
+        # Normalize advantages
+        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+
+        # We put - in front because we want to maximize the surrogate objective
+        policy_loss = -advantages * torch.exp(model_logprobs - fixed_logprobs)
+
+        return policy_loss.mean() - policy_entropy * self.entropy_coefficient
+
+    def metrics(self) -> list:
+        """ List of metrics to track for this learning process """
+        return [
+            AveragingNamedMetric("new_policy_loss", scope="model"),
+            AveragingNamedMetric("policy_entropy", scope="model"),
+            AveragingNamedMetric("value_loss", scope="model"),
+            AveragingNamedMetric("policy_optimization_success", scope="model"),
+            AveragingNamedMetric("policy_improvement_ratio", scope="model"),
+            AveragingNamedMetric("kl_divergence_step", scope="model"),
+            AveragingNamedMetric("policy_loss_improvement", scope="model"),
+            AveragingNamedMetric("advantage_norm", scope="model"),
+            AveragingNamedMetric("explained_variance", scope="model")
+        ]
+
+
+class TRPOFactory(ModuleFactory):
+    """ Factory class for policy gradient models """
+    def __init__(self, policy_net: ModuleFactory, value_net: ModuleFactory,
+                 max_kl, cg_iters, line_search_iters, cg_damping, entropy_coefficient, vf_iters,
+                 discount_factor, gae_lambda, improvement_acceptance_ratio, input_net: typing.Optional[ModuleFactory]):
+        self.policy_net = policy_net
+        self.value_net = value_net
+        self.input_net = input_net
+        self.entropy_coefficient = entropy_coefficient
+
+        self.mak_kl = max_kl
+        self.cg_iters = cg_iters
+        self.line_search_iters = line_search_iters
+        self.cg_damping = cg_damping
+        self.vf_iters = vf_iters
+        self.discount_factor = discount_factor
+        self.gae_lambda = gae_lambda
+        self.improvement_acceptance_ratio = improvement_acceptance_ratio
+
+    def instantiate(self, **extra_args):
+        """ Instantiate the model """
+        action_space = extra_args.pop('action_space')
+        observation_space = extra_args.pop('observation_space')
+
+        size_hint = gym_space_to_size_hint(observation_space)
+
+        if self.input_net is None:
+            input_net = None
+        else:
+            input_net = self.input_net.instantiate(size_hint=size_hint, **extra_args)
+            size_hint = input_net.size_hints()
+
+        policy_net = self.policy_net.instantiate(size_hint=size_hint, **extra_args)
+        value_net = self.value_net.instantiate(size_hint=size_hint, **extra_args)
+
+        return TRPO(
+            policy_net=policy_net,
+            value_net=value_net,
+            input_net=input_net,
+            action_space=action_space,
+            max_kl=self.mak_kl,
+            cg_iters=self.cg_iters,
+            line_search_iters=self.line_search_iters,
+            cg_damping=self.cg_damping,
+            entropy_coefficient=self.entropy_coefficient,
+            vf_iters=self.vf_iters,
+            discount_factor=self.discount_factor,
+            gae_lambda=self.gae_lambda,
+            improvement_acceptance_ratio=self.improvement_acceptance_ratio
+        )
+
+
+def create(policy_net: ModuleFactory, value_net: ModuleFactory,
+           max_kl, cg_iters, line_search_iters, cg_damping, entropy_coefficient, vf_iters,
+           discount_factor, gae_lambda, improvement_acceptance_ratio,
+           input_net: typing.Optional[ModuleFactory] = None):
+    """ Vel factory function """
+
+    return TRPOFactory(
+        policy_net=policy_net,
+        value_net=value_net,
+        input_net=input_net,
+        max_kl=max_kl,
+        cg_iters=cg_iters,
+        line_search_iters=line_search_iters,
+        cg_damping=cg_damping,
+        entropy_coefficient=entropy_coefficient,
+        vf_iters=vf_iters,
+        discount_factor=discount_factor,
+        gae_lambda=gae_lambda,
+        improvement_acceptance_ratio=improvement_acceptance_ratio,
+    )
diff --git a/vel/rl/reinforcer/__init__.py b/vel/rl/reinforcer/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/rl/reinforcers/buffered_mixed_policy_iteration_reinforcer.py b/vel/rl/reinforcer/buffered_mixed_policy_iteration_reinforcer.py
similarity index 72%
rename from vel/rl/reinforcers/buffered_mixed_policy_iteration_reinforcer.py
rename to vel/rl/reinforcer/buffered_mixed_policy_iteration_reinforcer.py
index 8f1888a6..548d1663 100644
--- a/vel/rl/reinforcers/buffered_mixed_policy_iteration_reinforcer.py
+++ b/vel/rl/reinforcer/buffered_mixed_policy_iteration_reinforcer.py
@@ -1,14 +1,14 @@
+import sys
 import attr
 import numpy as np
-import sys
 import torch
 import tqdm
 
-from vel.api import TrainingInfo, EpochInfo, BatchInfo, Model, ModelFactory
+from vel.api import TrainingInfo, EpochInfo, BatchInfo, ModuleFactory
 from vel.openai.baselines.common.vec_env import VecEnv
 from vel.rl.api import (
-    ReinforcerBase, ReinforcerFactory, VecEnvFactory, ReplayEnvRollerBase, AlgoBase, ReplayEnvRollerFactoryBase
-)
+    Reinforcer, ReinforcerFactory, VecEnvFactory, ReplayEnvRollerBase, ReplayEnvRollerFactoryBase,
+    RlPolicy)
 from vel.rl.metrics import (
     FPSMetric, EpisodeLengthMetric, EpisodeRewardMetricQuantile, EpisodeRewardMetric, FramesMetric
 )
@@ -22,7 +22,7 @@ class BufferedMixedPolicyIterationReinforcerSettings:
     stochastic_experience_replay: bool = True
 
 
-class BufferedMixedPolicyIterationReinforcer(ReinforcerBase):
+class BufferedMixedPolicyIterationReinforcer(Reinforcer):
     """
     A 'mixed' reinforcer that does both, on-policy learning from environment rollouts and off-policy learning
     from a replay buffer.
@@ -33,15 +33,19 @@ class BufferedMixedPolicyIterationReinforcer(ReinforcerBase):
     """
 
     def __init__(self, device: torch.device, settings: BufferedMixedPolicyIterationReinforcerSettings, env: VecEnv,
-                 model: Model, env_roller: ReplayEnvRollerBase, algo: AlgoBase) -> None:
+                 policy: RlPolicy, env_roller: ReplayEnvRollerBase) -> None:
         self.device = device
         self.settings = settings
 
         self.environment = env
-        self._trained_model = model.to(self.device)
+        self._model: RlPolicy = policy.to(self.device)
 
         self.env_roller = env_roller
-        self.algo = algo
+
+    @property
+    def policy(self) -> RlPolicy:
+        """ Model trained by this reinforcer """
+        return self._model
 
     def metrics(self) -> list:
         """ List of metrics to track for this learning process """
@@ -54,25 +58,16 @@ def metrics(self) -> list:
             EpisodeLengthMetric("episode_length")
         ]
 
-        return my_metrics + self.algo.metrics() + self.env_roller.metrics()
-
-    @property
-    def model(self) -> Model:
-        """ Model trained by this reinforcer """
-        return self._trained_model
+        return my_metrics + self.policy.metrics() + self.env_roller.metrics()
 
     def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
         """ Prepare models for training """
         if model_state is not None:
-            self.model.load_state_dict(model_state)
+            self.policy.load_state_dict(model_state)
         else:
-            self.model.reset_weights()
-
-        self.algo.initialize(
-            training_info=training_info, model=self.model, environment=self.environment, device=self.device
-        )
+            self.policy.reset_weights()
 
-    def train_epoch(self, epoch_info: EpochInfo, interactive=True):
+    def train_epoch(self, epoch_info: EpochInfo, interactive=True) -> None:
         """ Train model on an epoch of a fixed number of batch updates """
         epoch_info.on_epoch_begin()
 
@@ -84,14 +79,14 @@ def train_epoch(self, epoch_info: EpochInfo, interactive=True):
         for batch_idx in iterator:
             batch_info = BatchInfo(epoch_info, batch_idx)
 
-            batch_info.on_batch_begin()
+            batch_info.on_batch_begin('train')
             self.train_batch(batch_info)
-            batch_info.on_batch_end()
+            batch_info.on_batch_end('train')
 
         epoch_info.result_accumulator.freeze_results()
         epoch_info.on_epoch_end()
 
-    def train_batch(self, batch_info: BatchInfo):
+    def train_batch(self, batch_info: BatchInfo) -> None:
         """ Single, most atomic 'step' of learning this reinforcer can perform """
         batch_info['sub_batch_data'] = []
 
@@ -111,14 +106,14 @@ def train_batch(self, batch_info: BatchInfo):
 
     def on_policy_train_batch(self, batch_info: BatchInfo):
         """ Perform an 'on-policy' training step of evaluating an env and a single backpropagation step """
-        self.model.train()
+        rollout = self.env_roller.rollout(batch_info, self.settings.number_of_steps).to_device(self.device)
 
-        rollout = self.env_roller.rollout(batch_info, self.model, self.settings.number_of_steps).to_device(self.device)
+        # Preprocessing of the rollout for this policy
+        rollout = self.policy.process_rollout(rollout)
 
-        batch_result = self.algo.optimizer_step(
+        self.policy.train()
+        batch_result = self.policy.optimize(
             batch_info=batch_info,
-            device=self.device,
-            model=self.model,
             rollout=rollout
         )
 
@@ -128,14 +123,11 @@ def on_policy_train_batch(self, batch_info: BatchInfo):
 
     def off_policy_train_batch(self, batch_info: BatchInfo):
         """ Perform an 'off-policy' training step of sampling the replay buffer and gradient descent """
-        self.model.train()
-
-        rollout = self.env_roller.sample(batch_info, self.model, self.settings.number_of_steps).to_device(self.device)
+        rollout = self.env_roller.sample(batch_info, self.settings.number_of_steps).to_device(self.device)
 
-        batch_result = self.algo.optimizer_step(
+        self.policy.train()
+        batch_result = self.policy.optimize(
             batch_info=batch_info,
-            device=self.device,
-            model=self.model,
             rollout=rollout
         )
 
@@ -144,27 +136,24 @@ def off_policy_train_batch(self, batch_info: BatchInfo):
 
 class BufferedMixedPolicyIterationReinforcerFactory(ReinforcerFactory):
     """ Factory class for the PolicyGradientReplayBuffer factory """
-    def __init__(self, settings, env_factory: VecEnvFactory, model_factory: ModelFactory,
-                 env_roller_factory: ReplayEnvRollerFactoryBase, algo: AlgoBase, parallel_envs: int, seed: int):
+    def __init__(self, settings, env_factory: VecEnvFactory, model_factory: ModuleFactory,
+                 env_roller_factory: ReplayEnvRollerFactoryBase, parallel_envs: int, seed: int):
         self.settings = settings
 
         self.model_factory = model_factory
         self.env_factory = env_factory
         self.parallel_envs = parallel_envs
         self.env_roller_factory = env_roller_factory
-        self.algo = algo
         self.seed = seed
 
-    def instantiate(self, device: torch.device) -> ReinforcerBase:
+    def instantiate(self, device: torch.device) -> Reinforcer:
         env = self.env_factory.instantiate(parallel_envs=self.parallel_envs, seed=self.seed)
-        model = self.model_factory.instantiate(action_space=env.action_space)
-        env_roller = self.env_roller_factory.instantiate(env, device)
-
-        return BufferedMixedPolicyIterationReinforcer(device, self.settings, env, model, env_roller, self.algo)
+        policy = self.model_factory.instantiate(action_space=env.action_space, observation_space=env.observation_space)
+        env_roller = self.env_roller_factory.instantiate(environment=env, policy=policy, device=device)
+        return BufferedMixedPolicyIterationReinforcer(device, self.settings, env, policy, env_roller)
 
 
-def create(model_config, model, vec_env, algo, env_roller,
-           parallel_envs, number_of_steps,
+def create(model_config, model, vec_env, env_roller, parallel_envs, number_of_steps,
            experience_replay=1, stochastic_experience_replay=True):
     """ Vel factory function """
     settings = BufferedMixedPolicyIterationReinforcerSettings(
@@ -179,6 +168,5 @@ def create(model_config, model, vec_env, algo, env_roller,
         model_factory=model,
         parallel_envs=parallel_envs,
         env_roller_factory=env_roller,
-        algo=algo,
         seed=model_config.seed
     )
diff --git a/vel/rl/reinforcers/buffered_off_policy_iteration_reinforcer.py b/vel/rl/reinforcer/buffered_off_policy_iteration_reinforcer.py
similarity index 76%
rename from vel/rl/reinforcers/buffered_off_policy_iteration_reinforcer.py
rename to vel/rl/reinforcer/buffered_off_policy_iteration_reinforcer.py
index f8fc81e9..f3dd3310 100644
--- a/vel/rl/reinforcers/buffered_off_policy_iteration_reinforcer.py
+++ b/vel/rl/reinforcer/buffered_off_policy_iteration_reinforcer.py
@@ -1,14 +1,13 @@
-import attr
 import sys
-import tqdm
-
+import attr
 import torch
+import tqdm
 
-from vel.api import TrainingInfo, EpochInfo, BatchInfo, Model, ModelFactory
+from vel.api import TrainingInfo, EpochInfo, BatchInfo, Model, ModuleFactory
 from vel.openai.baselines.common.vec_env import VecEnv
 from vel.rl.api import (
-    ReinforcerBase, ReinforcerFactory, ReplayEnvRollerBase, AlgoBase, VecEnvFactory, ReplayEnvRollerFactoryBase
-)
+    Reinforcer, ReinforcerFactory, ReplayEnvRollerBase, VecEnvFactory, ReplayEnvRollerFactoryBase,
+    RlPolicy)
 from vel.rl.metrics import (
     FPSMetric, EpisodeLengthMetric, EpisodeRewardMetricQuantile, EpisodeRewardMetric, FramesMetric,
 )
@@ -26,19 +25,18 @@ class BufferedOffPolicyIterationReinforcerSettings:
     training_rounds: int = 1
 
 
-class BufferedOffPolicyIterationReinforcer(ReinforcerBase):
+class BufferedOffPolicyIterationReinforcer(Reinforcer):
     """
     An off-policy reinforcer that rolls out environment and stores transitions in a buffer.
     Afterwards, it samples experience batches from this buffer to train the policy.
     """
     def __init__(self, device: torch.device, settings: BufferedOffPolicyIterationReinforcerSettings,
-                 environment: VecEnv, model: Model, algo: AlgoBase, env_roller: ReplayEnvRollerBase):
+                 environment: VecEnv, policy: RlPolicy, env_roller: ReplayEnvRollerBase):
         self.device = device
         self.settings = settings
         self.environment = environment
 
-        self._trained_model = model.to(self.device)
-        self.algo = algo
+        self._policy = policy.to(self.device)
 
         self.env_roller = env_roller
 
@@ -53,22 +51,18 @@ def metrics(self) -> list:
             EpisodeLengthMetric("episode_length")
         ]
 
-        return my_metrics + self.algo.metrics() + self.env_roller.metrics()
+        return my_metrics + self.policy.metrics() + self.env_roller.metrics()
 
     @property
-    def model(self) -> Model:
-        return self._trained_model
+    def policy(self) -> Model:
+        return self._policy
 
     def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
         """ Prepare models for training """
         if model_state is not None:
-            self.model.load_state_dict(model_state)
+            self.policy.load_state_dict(model_state)
         else:
-            self.model.reset_weights()
-
-        self.algo.initialize(
-            training_info=training_info, model=self.model, environment=self.environment, device=self.device
-        )
+            self.policy.reset_weights()
 
     def train_epoch(self, epoch_info: EpochInfo, interactive=True) -> None:
         """ Train model for a single epoch  """
@@ -82,9 +76,9 @@ def train_epoch(self, epoch_info: EpochInfo, interactive=True) -> None:
         for batch_idx in iterator:
             batch_info = BatchInfo(epoch_info, batch_idx)
 
-            batch_info.on_batch_begin()
+            batch_info.on_batch_begin('train')
             self.train_batch(batch_info)
-            batch_info.on_batch_end()
+            batch_info.on_batch_end('train')
 
         epoch_info.result_accumulator.freeze_results()
         epoch_info.on_epoch_end()
@@ -96,22 +90,21 @@ def train_batch(self, batch_info: BatchInfo) -> None:
         For this reinforforcer, that involves:
 
         1. Roll out environment and store out experience in the buffer
-        2. Sample the buffer and train the algo on sample batch
+        2. Sample the buffer and train the policy on sample batch
         """
         # For each reinforcer batch:
 
         # 1. Roll out environment and store out experience in the buffer
         self.roll_out_and_store(batch_info)
 
-        # 2. Sample the buffer and train the algo on sample batch
+        # 2. Sample the buffer and train the policy on sample batch
         self.train_on_replay_memory(batch_info)
 
     def roll_out_and_store(self, batch_info):
         """ Roll out environment and store result in the replay buffer """
-        self.model.train()
-
         if self.env_roller.is_ready_for_sampling():
-            rollout = self.env_roller.rollout(batch_info, self.model, self.settings.rollout_steps).to_device(self.device)
+            rollout = self.env_roller.rollout(batch_info, self.settings.rollout_steps)
+            rollout = rollout.to_device(self.device)
 
             # Store some information about the rollout, no training phase
             batch_info['frames'] = rollout.frames()
@@ -122,7 +115,8 @@ def roll_out_and_store(self, batch_info):
 
             with tqdm.tqdm(desc="Populating memory", total=self.env_roller.initial_memory_size_hint()) as pbar:
                 while not self.env_roller.is_ready_for_sampling():
-                    rollout = self.env_roller.rollout(batch_info, self.model, self.settings.rollout_steps).to_device(self.device)
+                    rollout = self.env_roller.rollout(batch_info, self.settings.rollout_steps)
+                    rollout = rollout.to_device(self.device)
 
                     new_frames = rollout.frames()
                     frames += new_frames
@@ -136,18 +130,16 @@ def roll_out_and_store(self, batch_info):
 
     def train_on_replay_memory(self, batch_info):
         """ Train agent on a memory gotten from replay buffer """
-        self.model.train()
+        self.policy.train()
 
         # Algo will aggregate data into this list:
         batch_info['sub_batch_data'] = []
 
         for i in range(self.settings.training_rounds):
-            sampled_rollout = self.env_roller.sample(batch_info, self.model, self.settings.training_steps)
+            sampled_rollout = self.env_roller.sample(batch_info, self.settings.training_steps)
 
-            batch_result = self.algo.optimizer_step(
+            batch_result = self.policy.optimize(
                 batch_info=batch_info,
-                device=self.device,
-                model=self.model,
                 rollout=sampled_rollout.to_device(self.device)
             )
 
@@ -161,34 +153,32 @@ def train_on_replay_memory(self, batch_info):
 class BufferedOffPolicyIterationReinforcerFactory(ReinforcerFactory):
     """ Factory class for the DQN reinforcer """
 
-    def __init__(self, settings, env_factory: VecEnvFactory, model_factory: ModelFactory,
-                 algo: AlgoBase, env_roller_factory: ReplayEnvRollerFactoryBase, parallel_envs: int, seed: int):
+    def __init__(self, settings, env_factory: VecEnvFactory, model_factory: ModuleFactory,
+                 env_roller_factory: ReplayEnvRollerFactoryBase, parallel_envs: int, seed: int):
         self.settings = settings
 
         self.env_factory = env_factory
         self.model_factory = model_factory
-        self.algo = algo
         self.env_roller_factory = env_roller_factory
         self.parallel_envs = parallel_envs
         self.seed = seed
 
     def instantiate(self, device: torch.device) -> BufferedOffPolicyIterationReinforcer:
         env = self.env_factory.instantiate(parallel_envs=self.parallel_envs, seed=self.seed)
-        env_roller = self.env_roller_factory.instantiate(env, device)
-        model = self.model_factory.instantiate(action_space=env.action_space)
+        policy = self.model_factory.instantiate(action_space=env.action_space, observation_space=env.observation_space)
+        env_roller = self.env_roller_factory.instantiate(environment=env, policy=policy, device=device)
 
         return BufferedOffPolicyIterationReinforcer(
             device=device,
             settings=self.settings,
             environment=env,
-            model=model,
-            algo=self.algo,
+            policy=policy,
             env_roller=env_roller
         )
 
 
-def create(model_config, vec_env, model, algo, env_roller, parallel_envs: int,
-           rollout_steps: int, training_steps: int, training_rounds: int=1):
+def create(model_config, vec_env, model, env_roller, parallel_envs: int,
+           rollout_steps: int, training_steps: int, training_rounds: int = 1):
     """ Vel factory function """
     settings = BufferedOffPolicyIterationReinforcerSettings(
         rollout_steps=rollout_steps,
@@ -200,7 +190,6 @@ def create(model_config, vec_env, model, algo, env_roller, parallel_envs: int,
         settings=settings,
         env_factory=vec_env,
         model_factory=model,
-        algo=algo,
         env_roller_factory=env_roller,
         parallel_envs=parallel_envs,
         seed=model_config.seed
diff --git a/vel/rl/reinforcers/on_policy_iteration_reinforcer.py b/vel/rl/reinforcer/on_policy_iteration_reinforcer.py
similarity index 69%
rename from vel/rl/reinforcers/on_policy_iteration_reinforcer.py
rename to vel/rl/reinforcer/on_policy_iteration_reinforcer.py
index 46c7d450..b9455fd7 100644
--- a/vel/rl/reinforcers/on_policy_iteration_reinforcer.py
+++ b/vel/rl/reinforcer/on_policy_iteration_reinforcer.py
@@ -4,8 +4,12 @@
 import torch
 import tqdm
 
-from vel.api import Model, ModelFactory, TrainingInfo, EpochInfo, BatchInfo
-from vel.rl.api import ReinforcerBase, ReinforcerFactory, VecEnvFactory, EnvRollerFactoryBase, EnvRollerBase, AlgoBase
+from vel.api import ModuleFactory, TrainingInfo, EpochInfo, BatchInfo
+from vel.openai.baselines.common.vec_env import VecEnv
+from vel.rl.api import (
+    Reinforcer, ReinforcerFactory, VecEnvFactory, EnvRollerFactoryBase, EnvRollerBase,
+    RlPolicy
+)
 from vel.rl.metrics import (
     FPSMetric, EpisodeLengthMetric, EpisodeRewardMetricQuantile,
     EpisodeRewardMetric, FramesMetric
@@ -27,20 +31,24 @@ class OnPolicyIterationReinforcerSettings:
     shuffle_transitions: bool = True
 
 
-class OnPolicyIterationReinforcer(ReinforcerBase):
+class OnPolicyIterationReinforcer(Reinforcer):
     """
     A reinforcer that calculates on-policy environment rollouts and uses them to train policy directly.
     May split the sample into multiple batches and may replay batches a few times.
     """
-    def __init__(self, device: torch.device, settings: OnPolicyIterationReinforcerSettings, model: Model,
-                 algo: AlgoBase, env_roller: EnvRollerBase) -> None:
+    def __init__(self, device: torch.device, settings: OnPolicyIterationReinforcerSettings, policy: RlPolicy,
+                 env: VecEnv, env_roller: EnvRollerBase) -> None:
         self.device = device
         self.settings = settings
+        self.env = env
+        self.env_roller = env_roller
 
-        self._trained_model = model.to(self.device)
+        self._model: RlPolicy = policy.to(self.device)
 
-        self.env_roller = env_roller
-        self.algo = algo
+    @property
+    def policy(self) -> RlPolicy:
+        """ Model trained by this reinforcer """
+        return self._model
 
     def metrics(self) -> list:
         """ List of metrics to track for this learning process """
@@ -53,23 +61,17 @@ def metrics(self) -> list:
             EpisodeLengthMetric("episode_length"),
         ]
 
-        return my_metrics + self.algo.metrics() + self.env_roller.metrics()
-
-    @property
-    def model(self) -> Model:
-        """ Model trained by this reinforcer """
-        return self._trained_model
+        return my_metrics + self.env_roller.metrics() + self.policy.metrics()
 
     def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
         """ Prepare models for training """
         if model_state is not None:
-            self.model.load_state_dict(model_state)
+            self.policy.load_state_dict(model_state)
         else:
-            self.model.reset_weights()
-
-        self.algo.initialize(
-            training_info=training_info, model=self.model, environment=self.env_roller.environment, device=self.device
-        )
+            self.policy.reset_weights()
+            
+        # Register env in the training info
+        training_info['env'] = self.env
 
     def train_epoch(self, epoch_info: EpochInfo, interactive=True) -> None:
         """ Train model on an epoch of a fixed number of batch updates """
@@ -83,9 +85,9 @@ def train_epoch(self, epoch_info: EpochInfo, interactive=True) -> None:
         for batch_idx in iterator:
             batch_info = BatchInfo(epoch_info, batch_idx)
 
-            batch_info.on_batch_begin()
+            batch_info.on_batch_begin('train')
             self.train_batch(batch_info)
-            batch_info.on_batch_end()
+            batch_info.on_batch_end('train')
 
         epoch_info.result_accumulator.freeze_results()
         epoch_info.on_epoch_end()
@@ -94,18 +96,15 @@ def train_batch(self, batch_info: BatchInfo) -> None:
         """
         Batch - the most atomic unit of learning.
 
-        For this reinforforcer, that involves:
+        For this reinforcer, that involves:
 
         1. Roll out the environmnent using current policy
         2. Use that rollout to train the policy
         """
-        # Calculate environment rollout on the evaluation version of the model
-        self.model.train()
-
-        rollout = self.env_roller.rollout(batch_info, self.model, self.settings.number_of_steps)
+        rollout = self.env_roller.rollout(batch_info, self.settings.number_of_steps)
 
-        # Process rollout by the 'algo' (e.g. perform the advantage estimation)
-        rollout = self.algo.process_rollout(batch_info, rollout)
+        # Preprocessing of the rollout for this algorithm
+        rollout = self.policy.process_rollout(rollout)
 
         # Perform the training step
         # Algo will aggregate data into this list:
@@ -116,33 +115,32 @@ def train_batch(self, batch_info: BatchInfo) -> None:
 
         if self.settings.stochastic_experience_replay:
             # Always play experience at least once
-            experience_replay_count = max(np.random.poisson(self.settings.experience_replay), 1)
+            experience_replay_count = 1 + np.random.poisson(self.settings.experience_replay - 1)
         else:
             experience_replay_count = self.settings.experience_replay
 
+        self.policy.train()
+
         # Repeat the experience N times
         for i in range(experience_replay_count):
             # We may potentially need to split rollout into multiple batches
             if self.settings.batch_size >= rollout.frames():
-                batch_result = self.algo.optimizer_step(
+                metrics = self.policy.optimize(
                     batch_info=batch_info,
-                    device=self.device,
-                    model=self.model,
-                    rollout=rollout.to_device(self.device)
+                    rollout=rollout.to_device(self.device),
                 )
 
-                batch_info['sub_batch_data'].append(batch_result)
+                batch_info['sub_batch_data'].append(metrics)
             else:
                 # Rollout too big, need to split in batches
                 for batch_rollout in rollout.shuffled_batches(self.settings.batch_size):
-                    batch_result = self.algo.optimizer_step(
+
+                    metrics = self.policy.optimize(
                         batch_info=batch_info,
-                        device=self.device,
-                        model=self.model,
-                        rollout=batch_rollout.to_device(self.device)
+                        rollout=batch_rollout.to_device(self.device),
                     )
 
-                    batch_info['sub_batch_data'].append(batch_result)
+                    batch_info['sub_batch_data'].append(metrics)
 
         batch_info['frames'] = rollout.frames()
         batch_info['episode_infos'] = rollout.episode_information()
@@ -153,26 +151,24 @@ def train_batch(self, batch_info: BatchInfo) -> None:
 
 class OnPolicyIterationReinforcerFactory(ReinforcerFactory):
     """ Vel factory class for the PolicyGradientReinforcer """
-    def __init__(self, settings, parallel_envs: int, env_factory: VecEnvFactory, model_factory: ModelFactory,
-                 algo: AlgoBase, env_roller_factory: EnvRollerFactoryBase, seed: int):
+    def __init__(self, settings, parallel_envs: int, env_factory: VecEnvFactory, model_factory: ModuleFactory,
+                 env_roller_factory: EnvRollerFactoryBase, seed: int):
         self.settings = settings
         self.parallel_envs = parallel_envs
 
         self.env_factory = env_factory
         self.model_factory = model_factory
-        self.algo = algo
         self.env_roller_factory = env_roller_factory
         self.seed = seed
 
-    def instantiate(self, device: torch.device) -> ReinforcerBase:
+    def instantiate(self, device: torch.device) -> Reinforcer:
         env = self.env_factory.instantiate(parallel_envs=self.parallel_envs, seed=self.seed)
-        env_roller = self.env_roller_factory.instantiate(environment=env, device=device)
-        model = self.model_factory.instantiate(action_space=env.action_space)
-
-        return OnPolicyIterationReinforcer(device, self.settings, model, self.algo, env_roller)
+        policy = self.model_factory.instantiate(action_space=env.action_space, observation_space=env.observation_space)
+        env_roller = self.env_roller_factory.instantiate(environment=env, policy=policy, device=device)
+        return OnPolicyIterationReinforcer(device, self.settings, policy, env, env_roller)
 
 
-def create(model_config, model, vec_env, algo, env_roller, parallel_envs, number_of_steps,
+def create(model_config, model, vec_env, env_roller, parallel_envs, number_of_steps,
            batch_size=256, experience_replay=1, stochastic_experience_replay=False, shuffle_transitions=True):
     """ Vel factory function """
     settings = OnPolicyIterationReinforcerSettings(
@@ -188,7 +184,6 @@ def create(model_config, model, vec_env, algo, env_roller, parallel_envs, number
         parallel_envs=parallel_envs,
         env_factory=vec_env,
         model_factory=model,
-        algo=algo,
         env_roller_factory=env_roller,
         seed=model_config.seed
     )
diff --git a/vel/rl/test/test_integration.py b/vel/rl/test/test_integration.py
deleted file mode 100644
index 42aac5e3..00000000
--- a/vel/rl/test/test_integration.py
+++ /dev/null
@@ -1,677 +0,0 @@
-import torch
-import torch.optim as optim
-
-from vel.modules.input.image_to_tensor import ImageToTensorFactory
-from vel.modules.input.normalize_observations import NormalizeObservationsFactory
-from vel.rl.buffers.circular_replay_buffer import CircularReplayBuffer
-from vel.rl.buffers.prioritized_circular_replay_buffer import PrioritizedCircularReplayBuffer
-from vel.rl.commands.rl_train_command import FrameTracker
-from vel.rl.env_roller.step_env_roller import StepEnvRoller
-from vel.rl.env_roller.trajectory_replay_env_roller import TrajectoryReplayEnvRoller
-from vel.rl.env_roller.transition_replay_env_roller import TransitionReplayEnvRoller
-from vel.rl.metrics import EpisodeRewardMetric
-from vel.rl.modules.noise.eps_greedy import EpsGreedy
-from vel.rl.modules.noise.ou_noise import OuNoise
-from vel.schedules.linear import LinearSchedule
-from vel.schedules.linear_and_constant import LinearAndConstantSchedule
-from vel.util.random import set_seed
-
-from vel.rl.env.classic_atari import ClassicAtariEnv
-from vel.rl.env.mujoco import MujocoEnv
-from vel.rl.vecenv.subproc import SubprocVecEnvWrapper
-from vel.rl.vecenv.dummy import DummyVecEnvWrapper
-
-from vel.rl.models.stochastic_policy_model import StochasticPolicyModelFactory
-from vel.rl.models.q_stochastic_policy_model import QStochasticPolicyModelFactory
-from vel.rl.models.q_model import QModelFactory
-from vel.rl.models.deterministic_policy_model import DeterministicPolicyModelFactory
-from vel.rl.models.stochastic_policy_model_separate import StochasticPolicyModelSeparateFactory
-
-from vel.rl.models.backbone.nature_cnn import NatureCnnFactory
-from vel.rl.models.backbone.mlp import MLPFactory
-
-from vel.rl.reinforcers.on_policy_iteration_reinforcer import (
-    OnPolicyIterationReinforcer, OnPolicyIterationReinforcerSettings
-)
-
-from vel.rl.reinforcers.buffered_off_policy_iteration_reinforcer import (
-    BufferedOffPolicyIterationReinforcer, BufferedOffPolicyIterationReinforcerSettings
-)
-
-from vel.rl.reinforcers.buffered_mixed_policy_iteration_reinforcer import (
-    BufferedMixedPolicyIterationReinforcer, BufferedMixedPolicyIterationReinforcerSettings
-)
-
-from vel.rl.algo.dqn import DeepQLearning
-from vel.rl.algo.policy_gradient.a2c import A2CPolicyGradient
-from vel.rl.algo.policy_gradient.ppo import PpoPolicyGradient
-from vel.rl.algo.policy_gradient.trpo import TrpoPolicyGradient
-from vel.rl.algo.policy_gradient.acer import AcerPolicyGradient
-from vel.rl.algo.policy_gradient.ddpg import DeepDeterministicPolicyGradient
-
-from vel.api.info import TrainingInfo, EpochInfo
-
-
-CPU_DEVICE = torch.device('cpu')
-
-
-def test_a2c_breakout():
-    """
-    Simple 1 iteration of a2c breakout
-    """
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Create 16 environments evaluated in parallel in sub processess with all usual DeepMind wrappers
-    # These are just helper functions for that
-    vec_env = SubprocVecEnvWrapper(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=16, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model = StochasticPolicyModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    ).instantiate(action_space=vec_env.action_space)
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = OnPolicyIterationReinforcer(
-        device=CPU_DEVICE,
-        settings=OnPolicyIterationReinforcerSettings(
-            batch_size=256,
-            number_of_steps=5
-        ),
-        model=model,
-        algo=A2CPolicyGradient(
-            entropy_coefficient=0.01,
-            value_coefficient=0.5,
-            discount_factor=0.99,
-            max_grad_norm=0.5
-        ),
-        env_roller=StepEnvRoller(
-            environment=vec_env,
-            device=CPU_DEVICE
-        )
-    )
-
-    # Model optimizer
-    optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=7.0e-4, eps=1e-3)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = 1
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info, interactive=False)
-
-    training_info.on_train_end()
-
-
-def test_ppo_breakout():
-    """
-    Simple 1 iteration of ppo breakout
-    """
-    device = torch.device('cpu')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Create 16 environments evaluated in parallel in sub processess with all usual DeepMind wrappers
-    # These are just helper functions for that
-    vec_env = SubprocVecEnvWrapper(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=8, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model = StochasticPolicyModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    ).instantiate(action_space=vec_env.action_space)
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = OnPolicyIterationReinforcer(
-        device=device,
-        settings=OnPolicyIterationReinforcerSettings(
-            number_of_steps=12,
-            batch_size=4,
-            experience_replay=2,
-        ),
-        model=model,
-        algo=PpoPolicyGradient(
-            entropy_coefficient=0.01,
-            value_coefficient=0.5,
-            max_grad_norm=0.5,
-            cliprange=LinearSchedule(0.1, 0.0),
-            discount_factor=0.99,
-            normalize_advantage=True
-        ),
-        env_roller=StepEnvRoller(
-            environment=vec_env,
-            device=device,
-        )
-    )
-
-    # Model optimizer
-    # optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=7.0e-4, eps=1e-3)
-    optimizer = optim.Adam(reinforcer.model.parameters(), lr=2.5e-4, eps=1e-5)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[
-            FrameTracker(100_000)
-        ]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = 1
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info, interactive=False)
-
-    training_info.on_train_end()
-
-
-def test_dqn_breakout():
-    """
-    Simple 1 iteration of DQN breakout
-    """
-    device = torch.device('cpu')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Only single environment for DQN
-    vec_env = DummyVecEnvWrapper(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=1, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model_factory = QModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    )
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = BufferedOffPolicyIterationReinforcer(
-        device=device,
-        settings=BufferedOffPolicyIterationReinforcerSettings(
-            rollout_steps=4,
-            training_steps=1,
-        ),
-        environment=vec_env,
-        algo=DeepQLearning(
-            model_factory=model_factory,
-            double_dqn=False,
-            target_update_frequency=10_000,
-            discount_factor=0.99,
-            max_grad_norm=0.5
-        ),
-        model=model_factory.instantiate(action_space=vec_env.action_space),
-        env_roller=TransitionReplayEnvRoller(
-            environment=vec_env,
-            device=device,
-            replay_buffer=CircularReplayBuffer(
-                buffer_capacity=100,
-                buffer_initial_size=100,
-                num_envs=vec_env.num_envs,
-                observation_space=vec_env.observation_space,
-                action_space=vec_env.action_space,
-                frame_stack_compensation=True,
-                frame_history=4
-            ),
-            action_noise=EpsGreedy(
-                epsilon=LinearAndConstantSchedule(
-                    initial_value=1.0, final_value=0.1, end_of_interpolation=0.1
-                ),
-                environment=vec_env
-            )
-        )
-    )
-
-    # Model optimizer
-    optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=2.5e-4, alpha=0.95, momentum=0.95, eps=1e-3)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[
-            FrameTracker(100_000)
-        ]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = 1
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info, interactive=False)
-
-    training_info.on_train_end()
-
-
-def test_prioritized_dqn_breakout():
-    """
-    Simple 1 iteration of DQN prioritized replay breakout
-    """
-    device = torch.device('cpu')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Only single environment for DQN
-    vec_env = DummyVecEnvWrapper(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=1, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model_factory = QModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    )
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = BufferedOffPolicyIterationReinforcer(
-        device=device,
-        settings=BufferedOffPolicyIterationReinforcerSettings(
-            rollout_steps=4,
-            training_steps=1,
-        ),
-        environment=vec_env,
-        algo=DeepQLearning(
-            model_factory=model_factory,
-            double_dqn=False,
-            target_update_frequency=10_000,
-            discount_factor=0.99,
-            max_grad_norm=0.5
-        ),
-        model=model_factory.instantiate(action_space=vec_env.action_space),
-        env_roller=TransitionReplayEnvRoller(
-            environment=vec_env,
-            device=device,
-            replay_buffer=PrioritizedCircularReplayBuffer(
-                buffer_capacity=100,
-                buffer_initial_size=100,
-                num_envs=vec_env.num_envs,
-                observation_space=vec_env.observation_space,
-                action_space=vec_env.action_space,
-                priority_exponent=0.6,
-                priority_weight=LinearSchedule(
-                    initial_value=0.4,
-                    final_value=1.0
-                ),
-                priority_epsilon=1.0e-6,
-                frame_stack_compensation=True,
-                frame_history=4
-            ),
-            action_noise=EpsGreedy(
-                epsilon=LinearAndConstantSchedule(
-                    initial_value=1.0, final_value=0.1, end_of_interpolation=0.1
-                ),
-                environment=vec_env
-            )
-        )
-    )
-
-    # Model optimizer
-    optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=2.5e-4, alpha=0.95, momentum=0.95, eps=1e-3)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[
-            FrameTracker(100_000)
-        ]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = 1
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info, interactive=False)
-
-    training_info.on_train_end()
-
-
-def test_ddpg_bipedal_walker():
-    """
-    1 iteration of DDPG bipedal walker environment
-    """
-    device = torch.device('cpu')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Only single environment for DDPG
-
-    vec_env = DummyVecEnvWrapper(
-        MujocoEnv('BipedalWalker-v2')
-    ).instantiate(parallel_envs=1, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model_factory = DeterministicPolicyModelFactory(
-        input_block=NormalizeObservationsFactory(input_shape=24),
-        policy_backbone=MLPFactory(input_length=24, hidden_layers=[64, 64], normalization='layer'),
-        value_backbone=MLPFactory(input_length=28, hidden_layers=[64, 64], normalization='layer')
-    )
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = BufferedOffPolicyIterationReinforcer(
-        device=device,
-        settings=BufferedOffPolicyIterationReinforcerSettings(
-            rollout_steps=4,
-            training_steps=1,
-        ),
-        environment=vec_env,
-        algo=DeepDeterministicPolicyGradient(
-            model_factory=model_factory,
-            tau=0.01,
-            discount_factor=0.99,
-            max_grad_norm=0.5
-        ),
-        model=model_factory.instantiate(action_space=vec_env.action_space),
-        env_roller=TransitionReplayEnvRoller(
-            environment=vec_env,
-            device=device,
-            action_noise=OuNoise(std_dev=0.2, environment=vec_env),
-            replay_buffer=CircularReplayBuffer(
-                buffer_capacity=100,
-                buffer_initial_size=100,
-                num_envs=vec_env.num_envs,
-                observation_space=vec_env.observation_space,
-                action_space=vec_env.action_space
-            ),
-            normalize_returns=True,
-            discount_factor=0.99
-        ),
-    )
-
-    # Model optimizer
-    optimizer = optim.Adam(reinforcer.model.parameters(), lr=2.5e-4, eps=1e-4)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[
-            FrameTracker(100_000)
-        ]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = 1
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info, interactive=False)
-
-    training_info.on_train_end()
-
-
-def test_trpo_bipedal_walker():
-    """
-    1 iteration of TRPO on bipedal walker
-    """
-    device = torch.device('cpu')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    vec_env = DummyVecEnvWrapper(
-        MujocoEnv('BipedalWalker-v2', normalize_returns=True),
-    ).instantiate(parallel_envs=8, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model_factory = StochasticPolicyModelSeparateFactory(
-        input_block=NormalizeObservationsFactory(input_shape=24),
-        policy_backbone=MLPFactory(input_length=24, hidden_layers=[32, 32]),
-        value_backbone=MLPFactory(input_length=24, hidden_layers=[32])
-    )
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = OnPolicyIterationReinforcer(
-        device=device,
-        settings=OnPolicyIterationReinforcerSettings(
-            number_of_steps=12,
-        ),
-        model=model_factory.instantiate(action_space=vec_env.action_space),
-        algo=TrpoPolicyGradient(
-            max_kl=0.01,
-            cg_iters=10,
-            line_search_iters=10,
-            improvement_acceptance_ratio=0.1,
-            cg_damping=0.1,
-            vf_iters=5,
-            entropy_coef=0.0,
-            discount_factor=0.99,
-            max_grad_norm=0.5,
-            gae_lambda=1.0
-        ),
-        env_roller=StepEnvRoller(
-            environment=vec_env,
-            device=device,
-        )
-    )
-
-    # Model optimizer
-    optimizer = optim.Adam(reinforcer.model.parameters(), lr=1.0e-3, eps=1e-4)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[
-            FrameTracker(100_000)
-        ]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = 1
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info, interactive=False)
-
-    training_info.on_train_end()
-
-
-def test_acer_breakout():
-    """
-    1 iteration of ACER on breakout environment
-    """
-    device = torch.device('cpu')
-    seed = 1001
-
-    # Set random seed in python std lib, numpy and pytorch
-    set_seed(seed)
-
-    # Create 16 environments evaluated in parallel in sub processess with all usual DeepMind wrappers
-    # These are just helper functions for that
-    vec_env = SubprocVecEnvWrapper(
-        ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4
-    ).instantiate(parallel_envs=16, seed=seed)
-
-    # Again, use a helper to create a model
-    # But because model is owned by the reinforcer, model should not be accessed using this variable
-    # but from reinforcer.model property
-    model_factory = QStochasticPolicyModelFactory(
-        input_block=ImageToTensorFactory(),
-        backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4)
-    )
-
-    # Reinforcer - an object managing the learning process
-    reinforcer = BufferedMixedPolicyIterationReinforcer(
-        device=device,
-        settings=BufferedMixedPolicyIterationReinforcerSettings(
-            experience_replay=2,
-            number_of_steps=12,
-            stochastic_experience_replay=False
-        ),
-        model=model_factory.instantiate(action_space=vec_env.action_space),
-        env=vec_env,
-        algo=AcerPolicyGradient(
-            model_factory=model_factory,
-            entropy_coefficient=0.01,
-            q_coefficient=0.5,
-            rho_cap=10.0,
-            retrace_rho_cap=1.0,
-            trust_region=True,
-            trust_region_delta=1.0,
-            discount_factor=0.99,
-            max_grad_norm=10.0,
-        ),
-        env_roller=TrajectoryReplayEnvRoller(
-            environment=vec_env,
-            device=device,
-            replay_buffer=CircularReplayBuffer(
-                buffer_capacity=100,
-                buffer_initial_size=100,
-                num_envs=vec_env.num_envs,
-                action_space=vec_env.action_space,
-                observation_space=vec_env.observation_space,
-                frame_stack_compensation=True,
-                frame_history=4,
-            )
-        ),
-    )
-
-    # Model optimizer
-    optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=7.0e-4, eps=1e-3, alpha=0.99)
-
-    # Overall information store for training information
-    training_info = TrainingInfo(
-        metrics=[
-            EpisodeRewardMetric('episode_rewards'),  # Calculate average reward from episode
-        ],
-        callbacks=[]  # Print live metrics every epoch to standard output
-    )
-
-    # A bit of training initialization bookkeeping...
-    training_info.initialize()
-    reinforcer.initialize_training(training_info)
-    training_info.on_train_begin()
-
-    # Let's make 100 batches per epoch to average metrics nicely
-    num_epochs = 1
-
-    # Normal handrolled training loop
-    for i in range(1, num_epochs+1):
-        epoch_info = EpochInfo(
-            training_info=training_info,
-            global_epoch_idx=i,
-            batches_per_epoch=1,
-            optimizer=optimizer
-        )
-
-        reinforcer.train_epoch(epoch_info, interactive=False)
-
-    training_info.on_train_end()
diff --git a/vel/rl/util/actor.py b/vel/rl/util/actor.py
new file mode 100644
index 00000000..e23097d8
--- /dev/null
+++ b/vel/rl/util/actor.py
@@ -0,0 +1,52 @@
+import torch
+
+from vel.rl.api import RlPolicy
+from vel.util.tensor_util import to_device
+
+
+class PolicyActor:
+    """ Evaluates policy on a fixed set of environments. Additionally tracks the state """
+
+    def __init__(self, num_envs: int, policy: RlPolicy, device: torch.device):
+        self.num_envs = num_envs
+        self.policy = policy.to(device)
+        self.device = device
+        self.state = to_device(self.policy.zero_state(num_envs), self.device)
+
+    @property
+    def discount_factor(self) -> float:
+        return self.policy.discount_factor
+
+    def act(self, observation, advance_state=True, deterministic=False):
+        """ Return result of a policy on a given input """
+        result = self.policy.act(observation, state=self.state, deterministic=deterministic)
+
+        if self.policy.is_stateful and advance_state:
+            self.state = result['state']
+
+        return result
+
+    def reset_states(self, dones: torch.Tensor):
+        """ Reset states given dones """
+        self.policy.reset_episodic_state(dones)
+
+        if not self.policy.is_stateful:
+            return
+
+        dones = dones.to(self.device)
+        self.state = self.policy.reset_state(self.state, dones)
+
+    def value(self, observation):
+        """ Return value for provided observations """
+        return self.policy.value(observation, state=self.state)
+
+    @property
+    def is_stateful(self) -> bool:
+        """ If the model has a state that needs to be fed between individual observations """
+        return self.policy.is_stateful
+
+    def eval(self):
+        self.policy.eval()
+
+    def train(self):
+        self.policy.train()
diff --git a/vel/rl/vecenv/dummy.py b/vel/rl/vecenv/dummy.py
index b37f6e27..4df8cd2b 100644
--- a/vel/rl/vecenv/dummy.py
+++ b/vel/rl/vecenv/dummy.py
@@ -2,6 +2,7 @@
 from vel.openai.baselines.common.atari_wrappers import FrameStack
 from vel.openai.baselines.common.vec_env.dummy_vec_env import DummyVecEnv
 from vel.openai.baselines.common.vec_env.vec_frame_stack import VecFrameStack
+from vel.openai.baselines.common.vec_env.vec_normalize import VecNormalize
 
 from vel.rl.api import VecEnvFactory
 
@@ -9,9 +10,10 @@
 class DummyVecEnvWrapper(VecEnvFactory):
     """ Wraps a single-threaded environment into a one-element vector environment """
 
-    def __init__(self, env, frame_history=None):
+    def __init__(self, env, frame_history=None, normalize_returns=False):
         self.env = env
         self.frame_history = frame_history
+        self.normalize_returns = normalize_returns
 
     def instantiate(self, parallel_envs, seed=0, preset='default') -> VecEnv:
         """ Create vectorized environments """
@@ -20,11 +22,14 @@ def instantiate(self, parallel_envs, seed=0, preset='default') -> VecEnv:
         if self.frame_history is not None:
             envs = VecFrameStack(envs, self.frame_history)
 
+        if self.normalize_returns:
+            envs = VecNormalize(envs, ob=False, ret=True)
+
         return envs
 
     def instantiate_single(self, seed=0, preset='default'):
         """ Create a new Env instance - single """
-        env = self.env.instantiate(seed=seed, serial_id=0, preset=preset)
+        env = self.env.instantiate()
 
         if self.frame_history is not None:
             env = FrameStack(env, self.frame_history)
@@ -33,9 +38,9 @@ def instantiate_single(self, seed=0, preset='default'):
 
     def _creation_function(self, idx, seed, preset):
         """ Helper function to create a proper closure around supplied values """
-        return lambda: self.env.instantiate(seed=seed, serial_id=idx, preset=preset)
+        return lambda: self.env.instantiate()
 
 
-def create(env, frame_history=None):
+def create(env, frame_history=None, normalize_returns=False):
     """ Vel factory function """
-    return DummyVecEnvWrapper(env, frame_history=frame_history)
+    return DummyVecEnvWrapper(env, frame_history=frame_history, normalize_returns=normalize_returns)
diff --git a/vel/scheduler/ladder.py b/vel/scheduler/ladder.py
index 0699c266..780ed013 100644
--- a/vel/scheduler/ladder.py
+++ b/vel/scheduler/ladder.py
@@ -2,7 +2,7 @@
 import numpy as np
 
 
-from vel.api import Callback, SchedulerFactory
+from vel.api import Callback, SchedulerFactory, EpochInfo
 
 
 class LadderScheduler(Callback):
@@ -16,7 +16,7 @@ def lambda_fn(self, epoch_idx):
         idx = np.minimum(np.searchsorted(self.schedule_limits, epoch_idx), len(self.schedule_limits) - 1)
         return self.schedule_numbers[idx]
 
-    def on_epoch_begin(self, epoch_info):
+    def on_epoch_end(self, epoch_info: EpochInfo) -> None:
         self.scheduler.step(epoch=epoch_info.global_epoch_idx)
 
 
diff --git a/vel/scheduler/linear_batch_scaler.py b/vel/scheduler/linear_batch_scaler.py
index 59088401..203b0980 100644
--- a/vel/scheduler/linear_batch_scaler.py
+++ b/vel/scheduler/linear_batch_scaler.py
@@ -1,3 +1,5 @@
+import typing
+
 import vel.api as base
 
 from vel.api import BatchInfo, TrainingInfo
@@ -18,7 +20,7 @@ def write_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict)
     def load_state_dict(self, training_info: TrainingInfo, hidden_state_dict: dict):
         self.starting_lr = hidden_state_dict['linear_batch_scaler/starting_lr']
 
-    def on_batch_begin(self, batch_info: BatchInfo):
+    def on_batch_begin(self, batch_info: BatchInfo, dataset: typing.Optional[str] = None):
         for starting_lr, param_group in zip(self.starting_lr, self.optimizer.param_groups):
             param_group['lr'] = starting_lr * (1.0 - batch_info['progress'])
 
@@ -32,5 +34,3 @@ def instantiate(self, optimizer, last_epoch=-1) -> LinearBatchScaler:
 def create():
     """ Vel factory function """
     return LinearBatchScalerFactory()
-
-
diff --git a/vel/scheduler/multi_step.py b/vel/scheduler/multi_step.py
index 172f7ee7..6ab8110b 100644
--- a/vel/scheduler/multi_step.py
+++ b/vel/scheduler/multi_step.py
@@ -1,23 +1,30 @@
 import torch.optim.lr_scheduler as scheduler
 
+from vel.api import Callback, SchedulerFactory, EpochInfo
 
-# class MultiStepScheduler:
-#     def __init__(self, optimizer, milestones, gamma, last_epoch):
-#         self.scheduler = scheduler.MultiStepLR(optimizer, milestones, gamma, last_epoch=last_epoch)
-#
-#     def get_lr(self):
-#         return self.scheduler.get_lr()
-#
-#     def pre_epoch_step(self, epoch_idx):
-#         self.scheduler.step()
-#
-#     def post_epoch_step(self, epoch_idx, metrics):
-#         pass
-#
-#
-# def create(milestones, gamma=0.1):
-#     """ Create a multi-step scheduler """
-#     def scheduler_fn(optimizer, last_epoch=-1):
-#         return MultiStepScheduler(optimizer, milestones, gamma, last_epoch=last_epoch)
-#
-#     return scheduler_fn
+
+class MultiStepScheduler(Callback):
+    """ Scheduler multiplying the learning rate given number after given number of epochs """
+    def __init__(self, optimizer, milestones, gamma, last_epoch):
+        self.scheduler = scheduler.MultiStepLR(optimizer, milestones, gamma, last_epoch=last_epoch)
+
+    def get_lr(self):
+        return self.scheduler.get_lr()
+
+    def on_epoch_end(self, epoch_info: EpochInfo) -> None:
+        self.scheduler.step(epoch=epoch_info.global_epoch_idx)
+
+
+class MultiStepSchedulerFactory(SchedulerFactory):
+    """ Factory class for ladder scheduler """
+    def __init__(self, milestones, gamma):
+        self.milestones = milestones
+        self.gamma = gamma
+
+    def instantiate(self, optimizer, last_epoch=-1) -> MultiStepScheduler:
+        return MultiStepScheduler(optimizer, self.milestones, self.gamma, last_epoch)
+
+
+def create(milestones, gamma=0.1):
+    """ Create a multi-step scheduler """
+    return MultiStepSchedulerFactory(milestones, gamma)
diff --git a/vel/scheduler/reduce_lr_on_plateau.py b/vel/scheduler/reduce_lr_on_plateau.py
index daf97999..98dc67a3 100644
--- a/vel/scheduler/reduce_lr_on_plateau.py
+++ b/vel/scheduler/reduce_lr_on_plateau.py
@@ -1,8 +1,9 @@
-import torch.optim.lr_scheduler as scheduler
+# import torch.optim.lr_scheduler as scheduler
 
 
 # class ReduceLrOnPlateau:
-#     def __init__(self, optimizer, metric_name, mode, factor, patience, threshold, threshold_mode, cooldown, min_lr, epsilon):
+#     def __init__(self, optimizer, metric_name, mode, factor, patience, threshold, threshold_mode,
+#     cooldown, min_lr, epsilon):
 #         self.metric_name = metric_name
 #         self.scheduler = scheduler.ReduceLROnPlateau(
 #             optimizer,
@@ -30,7 +31,7 @@
 #            min_lr=0, epsilon=1e-8):
 #     """ Create a scheduler that lowers the LR on metric plateau """
 #     def scheduler_fn(optimizer):
-#         return ReduceLrOnPlateau(optimizer, metric_name, mode, factor, patience, threshold, threshold_mode, cooldown, min_lr, epsilon)
+#         return ReduceLrOnPlateau(optimizer, metric_name, mode, factor, patience, threshold, threshold_mode,
+#         cooldown, min_lr, epsilon)
 #
 #     return scheduler_fn
-
diff --git a/vel/sources/img_dir_source.py b/vel/sources/img_dir_source.py
deleted file mode 100644
index c1df3120..00000000
--- a/vel/sources/img_dir_source.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os.path
-
-import torchvision.datasets as ds
-
-from vel.api import TrainingData
-
-
-class ImageDirSource(ds.ImageFolder):
-    pass
-
-
-def create(model_config, path, num_workers, batch_size, augmentations=None, tta=None):
-    """ Create an ImageDirSource with supplied arguments """
-    if not os.path.isabs(path):
-        path = model_config.project_top_dir(path)
-
-    train_path = os.path.join(path, 'train')
-    valid_path = os.path.join(path, 'valid')
-
-    train_ds = ImageDirSource(train_path)
-    val_ds = ImageDirSource(valid_path)
-
-    return TrainingData(
-        train_ds,
-        val_ds,
-        num_workers=num_workers,
-        batch_size=batch_size,
-        augmentations=augmentations,
-        # test_time_augmentation=tta
-    )
diff --git a/vel/sources/nlp/text_url.py b/vel/sources/nlp/text_url.py
deleted file mode 100644
index e1b2095d..00000000
--- a/vel/sources/nlp/text_url.py
+++ /dev/null
@@ -1,200 +0,0 @@
-import certifi
-import numpy as np
-import os
-import pathlib
-import urllib3
-
-import torch
-
-from vel.api import Source
-
-
-class TextIterator:
-    """ Iterator over a text dataset """
-    def __init__(self, padded_sequence, sequence_length, batch_size, alphabet_size, num_batches):
-        self.sequence_length = sequence_length
-        self.batch_size = batch_size
-        self.num_batches = num_batches
-        self.alphabet_size = alphabet_size
-
-        self.padded_sequence = padded_sequence[:-1].reshape(self.num_batches * self.batch_size, self.sequence_length)
-        self.padded_sequence_next = padded_sequence[1:].reshape(self.num_batches * self.batch_size, self.sequence_length)
-
-        self.sequence_indices = np.arange(self.num_batches * self.batch_size)
-
-        np.random.shuffle(self.sequence_indices)
-
-        self.sequence_indices = self.sequence_indices.reshape(self.num_batches, self.batch_size)
-
-        self.batch_idx = 0
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.batch_idx == self.num_batches:
-            raise StopIteration
-        else:
-            input_data = torch.from_numpy(self.padded_sequence[self.sequence_indices[self.batch_idx]])
-            target_data = torch.from_numpy(self.padded_sequence_next[self.sequence_indices[self.batch_idx]])
-
-            self.batch_idx += 1
-
-            return input_data.to(torch.long), target_data.to(torch.long)
-
-
-class TextLoader:
-    """ Loader of sequential text data """
-    def __init__(self, sequence, sequence_length, batch_size, alphabet_size):
-        self.sequence = sequence
-        self.sequence_length = sequence_length
-        self.batch_size = batch_size
-        self.alphabet_size = alphabet_size
-
-        # 1 is for the last element as the target needs to be shifted by 1
-        residual_length = (len(self.sequence) - self.sequence_length - 1)
-        full_size = self.sequence_length * self.batch_size
-
-        rest = residual_length % full_size
-        self.num_batches = residual_length // full_size
-
-        if rest > 0:
-            self.sequence = np.pad(self.sequence, (0, full_size - rest), mode='constant')
-            self.num_batches += 1
-
-    def __iter__(self):
-        initial_offset = np.random.randint(self.sequence_length)
-        relevant_subsequence = self.sequence[
-           # 1 is for the last element as the target needs to be shifted by 1
-           initial_offset:self.num_batches * self.sequence_length * self.batch_size + initial_offset + 1
-        ]
-
-        return TextIterator(
-            relevant_subsequence, self.sequence_length, self.batch_size,
-            alphabet_size=self.alphabet_size,
-            num_batches=self.num_batches
-        )
-
-    def __len__(self):
-        """ Number of batches in this loader """
-        return self.num_batches
-
-
-class TextUrlSource(Source):
-    """ Download text from source and model it character by character """
-    def __init__(self, url, absolute_data_path, sequence_length, batch_size, train_val_split=0.8):
-        super().__init__()
-
-        self.url = url
-        self.data_path = absolute_data_path
-        self.sequence_length = sequence_length
-        self.batch_size = batch_size
-        self.train_val_split = train_val_split
-
-        self.text_path = os.path.join(self.data_path, 'text.txt')
-        self.processed_path = os.path.join(self.data_path, 'processed.data')
-
-        self.data_dict = self.download()
-
-        content_encoded = self.data_dict['content_encoded']
-        alphabet_size = len(self.data_dict['alphabet'])
-
-        split_idx = int(len(content_encoded) * train_val_split)
-
-        self._train_loader = TextLoader(
-            sequence=content_encoded[:split_idx],
-            sequence_length=sequence_length,
-            batch_size=batch_size,
-            alphabet_size=alphabet_size,
-        )
-
-        self._val_loader = TextLoader(
-            sequence=content_encoded[split_idx:],
-            sequence_length=sequence_length,
-            batch_size=batch_size,
-            alphabet_size=alphabet_size,
-        )
-
-    def encode_character(self, char):
-        return self.data_dict['character_to_index'][char]
-
-    def decode_character(self, index):
-        return self.data_dict['index_to_character'][index]
-
-    def train_loader(self):
-        """ PyTorch loader of training data """
-        return self._train_loader
-
-    def val_loader(self):
-        """ PyTorch loader of validation data """
-        return self._val_loader
-
-    def train_dataset(self):
-        """ Return the training dataset """
-        return None
-
-    def val_dataset(self):
-        """ Return the validation dataset """
-        return None
-
-    def train_iterations_per_epoch(self):
-        """ Return number of iterations per epoch """
-        return len(self._train_loader)
-
-    def val_iterations_per_epoch(self):
-        """ Return number of iterations per epoch - validation """
-        return len(self._val_loader)
-
-    def download(self):
-        """ Make sure data file is downloaded and stored properly """
-        if not os.path.exists(self.data_path):
-            # Create if it doesn't exist
-            pathlib.Path(self.data_path).mkdir(parents=True, exist_ok=True)
-
-        if not os.path.exists(self.text_path):
-            http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED', ca_certs=certifi.where())
-
-            with open(self.text_path, 'wt') as fp:
-                request = http.request('GET', self.url)
-                content = request.data.decode('utf8')
-                fp.write(content)
-
-        if not os.path.exists(self.processed_path):
-            with open(self.text_path, 'rt') as fp:
-                content = fp.read()
-
-            alphabet = sorted(set(content))
-
-            index_to_character = {idx: c for idx, c in enumerate(alphabet, 1)}
-            character_to_index = {c: idx for idx, c in enumerate(alphabet, 1)}
-
-            content_encoded = np.array([character_to_index[c] for c in content], dtype=np.uint8)
-
-            data_dict = {
-                'alphabet': alphabet,
-                'index_to_character': index_to_character,
-                'character_to_index': character_to_index,
-                'content_encoded': content_encoded
-            }
-
-            with open(self.processed_path, 'wb') as fp:
-                torch.save(data_dict, fp)
-        else:
-            with open(self.processed_path, 'rb') as fp:
-                data_dict = torch.load(fp)
-
-        return data_dict
-
-
-def create(model_config, url, local_dir, sequence_length=64, batch_size=64, train_val_split=0.8):
-    """ Vel factory function """
-    if not os.path.isabs(local_dir):
-        local_dir = model_config.project_data_dir(local_dir)
-
-    return TextUrlSource(
-        url,
-        absolute_data_path=local_dir,
-        sequence_length=sequence_length,
-        batch_size=batch_size,
-        train_val_split=train_val_split,
-    )
diff --git a/vel/sources/vision/cifar10.py b/vel/sources/vision/cifar10.py
deleted file mode 100644
index 452f5eb7..00000000
--- a/vel/sources/vision/cifar10.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from torchvision import datasets
-
-from vel.api import TrainingData
-
-from vel.augmentations.normalize import Normalize
-from vel.augmentations.to_tensor import ToTensor
-from vel.augmentations.to_array import ToArray
-
-
-def create(model_config, batch_size, normalize=True, num_workers=0, augmentations=None):
-    """
-    Create a CIFAR10 dataset, normalized.
-    Augmentations are the same as in the literature benchmarking CIFAR performance.
-    """
-    path = model_config.data_dir('cifar10')
-
-    train_dataset = datasets.CIFAR10(path, train=True, download=True)
-    test_dataset = datasets.CIFAR10(path, train=False, download=True)
-
-    augmentations = [ToArray()] + (augmentations if augmentations is not None else [])
-    
-    if normalize:
-        train_data = train_dataset.train_data
-        mean_value = (train_data / 255).mean(axis=(0, 1, 2))
-        std_value = (train_data / 255).std(axis=(0, 1, 2))
-
-        augmentations.append(Normalize(mean=mean_value, std=std_value, tags=['train', 'val']))
-
-    augmentations.append(ToTensor())
-
-    return TrainingData(
-        train_dataset,
-        test_dataset,
-        batch_size=batch_size,
-        num_workers=num_workers,
-        augmentations=augmentations
-    )
diff --git a/vel/sources/vision/mnist.py b/vel/sources/vision/mnist.py
deleted file mode 100644
index 24f26646..00000000
--- a/vel/sources/vision/mnist.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from torchvision import datasets
-
-
-from vel.api import TrainingData
-
-from vel.augmentations.normalize import Normalize
-from vel.augmentations.to_tensor import ToTensor
-from vel.augmentations.to_array import ToArray
-
-
-def create(model_config, batch_size, normalize=True, num_workers=0, augmentations=None):
-    """ Create a MNIST dataset, normalized """
-    path = model_config.data_dir('mnist')
-
-    train_dataset = datasets.MNIST(path, train=True, download=True)
-    test_dataset = datasets.MNIST(path, train=False, download=True)
-
-    augmentations = [ToArray()] + (augmentations if augmentations is not None else [])
-
-    if normalize:
-        train_data = train_dataset.train_data
-        mean_value = (train_data.double() / 255).mean().item()
-        std_value = (train_data.double() / 255).std().item()
-
-        augmentations.append(Normalize(mean=mean_value, std=std_value, tags=['train', 'val']))
-
-    augmentations.append(ToTensor())
-
-    return TrainingData(
-        train_dataset,
-        test_dataset,
-        num_workers=num_workers,
-        batch_size=batch_size,
-        augmentations=augmentations
-    )
diff --git a/vel/storage/backend/mongodb.py b/vel/storage/backend/mongodb.py
index d9d197eb..77b8f6da 100644
--- a/vel/storage/backend/mongodb.py
+++ b/vel/storage/backend/mongodb.py
@@ -34,7 +34,7 @@ def get_frame(self):
             return pd.DataFrame(metric_items).drop(['_id', 'model_name'], axis=1).set_index('epoch_idx')
 
     def store(self, metrics):
-        augmented_metrics = metrics.copy()
+        augmented_metrics = {'{}/{}/{}'.format(k.scope, k.dataset, k.name): v for k, v in metrics.items()}
 
         model_name = self.model_config.name
         run_name = self.model_config.run_name
@@ -48,4 +48,3 @@ def store(self, metrics):
 def create(model_config, uri, database):
     """ Vel factory function """
     return MongoDbBackend(model_config, uri, database)
-
diff --git a/vel/storage/classic.py b/vel/storage/classic.py
index de130c52..5815dca4 100644
--- a/vel/storage/classic.py
+++ b/vel/storage/classic.py
@@ -36,9 +36,10 @@ def load(self, train_info: TrainingInfo) -> (dict, dict):
         Resume learning process and return loaded hidden state dictionary
         """
         last_epoch = train_info.start_epoch_idx
+        device = self.model_config.torch_device()
 
-        model_state = torch.load(self.checkpoint_filename(last_epoch))
-        hidden_state = torch.load(self.checkpoint_hidden_filename(last_epoch))
+        model_state = torch.load(self.checkpoint_filename(last_epoch), map_location=device)
+        hidden_state = torch.load(self.checkpoint_hidden_filename(last_epoch), map_location=device)
 
         self.checkpoint_strategy.restore(hidden_state)
         train_info.restore(hidden_state)
@@ -135,6 +136,10 @@ def checkpoint_hidden_filename(self, epoch_idx) -> str:
         """ Return checkpoint filename for this model - hidden state """
         return self.model_config.checkpoint_dir('checkpoint_hidden_{:08}.data'.format(epoch_idx))
 
+    def last_checkpoint_filename(self) -> str:
+        """ return checkpoint filename for the last saved checkpoint """
+        return self.checkpoint_filename(self.last_epoch_idx())
+
     ####################################################################################################################
     # Internal interface
     def _persisted_last_epoch(self) -> int:
diff --git a/vel/storage/strategy/checkpoint_strategy.py b/vel/storage/strategy/checkpoint_strategy.py
index a2d245c4..272b3681 100644
--- a/vel/storage/strategy/checkpoint_strategy.py
+++ b/vel/storage/strategy/checkpoint_strategy.py
@@ -22,5 +22,3 @@ def current_best_checkpoint_idx(self) -> typing.Union[int, None]:
     def write_state_dict(self, hidden_state_dict): pass
 
     def restore(self, hidden_state_dict): pass
-
-
diff --git a/vel/storage/streaming/stdout.py b/vel/storage/streaming/stdout.py
index e4bc5fc3..fc53f23f 100644
--- a/vel/storage/streaming/stdout.py
+++ b/vel/storage/streaming/stdout.py
@@ -1,43 +1,48 @@
-from vel.api import EpochInfo, Callback
+from vel.api import EpochInfo, Callback, ModelConfig
 
 
 class StdoutStreaming(Callback):
     """ Stream results to stdout """
+    def __init__(self, model_config: ModelConfig):
+        self.model_config = model_config
+
     def on_epoch_end(self, epoch_info: EpochInfo):
-        if epoch_info.training_info.run_name:
-            print(f"=>>>>>>>>>> EPOCH {epoch_info.global_epoch_idx} [{epoch_info.training_info.run_name}]")
+        if self.model_config.tag:
+            tag = self.model_config.tag
+            print(f"=>>>>>>>>>> EPOCH {epoch_info.global_epoch_idx} [{self.model_config.run_name} - {tag}]")
         else:
-            print(f"=>>>>>>>>>> EPOCH {epoch_info.global_epoch_idx}")
+            print(f"=>>>>>>>>>> EPOCH {epoch_info.global_epoch_idx} [{self.model_config.run_name}]")
 
-        if any(':' not in x for x in epoch_info.result.keys()):
-            self._print_metrics_line(epoch_info.result, head=None)
+        if any(x.dataset is None for x in epoch_info.result.keys()):
+            self._print_metrics_line(epoch_info.result, dataset=None)
 
-        head_set = sorted({x.split(':')[0] + ':' for x in epoch_info.result.keys() if ':' in x})
+        head_set = sorted({x.dataset for x in epoch_info.result.keys() if x.dataset is not None})
 
         for head in head_set:
-            if any(x.startswith(head) for x in epoch_info.result.keys()):
-                self._print_metrics_line(epoch_info.result, head)
+            self._print_metrics_line(epoch_info.result, head)
 
         print(f"=>>>>>>>>>> DONE")
 
     @staticmethod
-    def _print_metrics_line(metrics, head=None):
-        if head is None:
-            head = 'Metrics:'
+    def _print_metrics_line(metrics, dataset=None):
+        if dataset is None:
+            dataset = 'Metrics:'
 
             metrics_list = [
-                "{} {:.06f}".format(k, metrics[k])
-                for k in sorted([k for k in metrics.keys() if ':' not in k])
+                "{}/{} {:.04f}".format(k.scope, k.name, metrics[k])
+                for k in sorted([k for k in metrics.keys() if k.dataset is None])
+                if k.metric_type == 'scalar'
             ]
         else:
             metrics_list = [
-                "{} {:.06f}".format(k.split(':')[1], metrics[k])
-                for k in sorted([k for k in metrics.keys() if k.startswith(head)])
+                "{}/{} {:.04f}".format(k.scope, k.name, metrics[k])
+                for k in sorted([k for k in metrics.keys() if k.dataset == dataset])
+                if k.metric_type == 'scalar'
             ]
 
-        print('{0: <10}'.format(head.capitalize()), " ".join(metrics_list))
+        print('{0: <10}'.format(dataset.capitalize()), " ".join(metrics_list))
 
 
-def create():
+def create(model_config):
     """ Vel factory function """
-    return StdoutStreaming()
+    return StdoutStreaming(model_config)
diff --git a/vel/storage/streaming/tensorboard.py b/vel/storage/streaming/tensorboard.py
new file mode 100644
index 00000000..52462d90
--- /dev/null
+++ b/vel/storage/streaming/tensorboard.py
@@ -0,0 +1,56 @@
+import os
+import shutil
+
+from vel.api import ModelConfig, Callback, TrainingInfo, EpochInfo, Model
+from torch.utils.tensorboard import SummaryWriter
+
+
+class TensorboardStreaming(Callback):
+    """ Stream results to tensorboard """
+
+    def __init__(self, model_config: ModelConfig, record_images=True):
+        self.model_config = model_config
+        self.logdir = self.model_config.output_dir('tensorboard', self.model_config.run_name)
+        self.record_images = record_images
+
+    def on_train_begin(self, training_info: TrainingInfo, model: Model) -> None:
+        """ Potentially cleanup previous runs """
+        if training_info.start_epoch_idx == 0:
+            if os.path.exists(self.logdir):
+                shutil.rmtree(self.logdir)
+
+    def on_epoch_end(self, epoch_info: EpochInfo):
+        """ Push data to tensorboard on push """
+        head_set = sorted({x.dataset for x in epoch_info.result.keys()})
+
+        for head in head_set:
+            if head is None:
+                summary_writer = SummaryWriter(log_dir=os.path.join(self.logdir, "generic"))
+            else:
+                summary_writer = SummaryWriter(log_dir=os.path.join(self.logdir, head))
+
+            for key, value in epoch_info.result.items():
+                if key.dataset == head:
+                    tag = '{}/{}'.format(key.scope, key.name)
+
+                    if key.metric_type == 'scalar':
+                        summary_writer.add_scalar(
+                            tag=tag,
+                            scalar_value=value,
+                            global_step=epoch_info.global_epoch_idx,
+                        )
+                    elif key.metric_type == 'image' and self.record_images:
+                        if value is not None:
+                            summary_writer.add_image(
+                                tag=tag,
+                                img_tensor=value,
+                                global_step=epoch_info.global_epoch_idx,
+                                dataformats='WHC'
+                            )
+
+            summary_writer.close()
+
+
+def create(model_config, record_images=True):
+    """ Vel factory function """
+    return TensorboardStreaming(model_config, record_images=record_images)
diff --git a/vel/storage/streaming/visdom.py b/vel/storage/streaming/visdom.py
index 917bb390..c7b95f29 100644
--- a/vel/storage/streaming/visdom.py
+++ b/vel/storage/streaming/visdom.py
@@ -21,7 +21,8 @@ def __init__(self, model_config: ModelConfig, visdom_settings: VisdomSettings):
 
     def on_epoch_end(self, epoch_info):
         """ Update data in visdom on push """
-        metrics_df = pd.DataFrame([epoch_info.result]).set_index('epoch_idx')
+        result = {k.format(): v for k, v in epoch_info.result.items() if k.metric_type == 'scalar'}
+        metrics_df = pd.DataFrame([result], index=[epoch_info.global_epoch_idx])
 
         visdom_append_metrics(
             self.vis,
@@ -29,14 +30,14 @@ def on_epoch_end(self, epoch_info):
             first_epoch=epoch_info.global_epoch_idx == 1
         )
 
-    def on_batch_end(self, batch_info):
+    def on_batch_end(self, batch_info, dataset=None):
         """ Stream LR to visdom """
         if self.settings.stream_lr:
             iteration_idx = (
-                    float(batch_info.epoch_number) +
-                    float(batch_info.batch_number) / batch_info.batches_per_epoch
+                float(batch_info.epoch_number) +
+                float(batch_info.batch_number) / batch_info.batches_per_epoch
             )
-            
+
             lr = batch_info.optimizer.param_groups[-1]['lr']
 
             metrics_df = pd.DataFrame([lr], index=[iteration_idx], columns=['lr'])
diff --git a/vel/storage/streaming/wandb.py b/vel/storage/streaming/wandb.py
new file mode 100644
index 00000000..78ea775c
--- /dev/null
+++ b/vel/storage/streaming/wandb.py
@@ -0,0 +1,48 @@
+import wandb
+import yaml
+
+
+from vel.api import ModelConfig, Callback, TrainingInfo, Model
+
+
+class WandbStreaming(Callback):
+    """ Stream live results from training to WandB """
+
+    def __init__(self, model_config: ModelConfig, register_model: bool = False, write_hyperparams: bool = True,
+                 wandb_config=None):
+        self.model_config = model_config
+        self.project = self.model_config.provide('project_name')
+        self.register_model = register_model
+        self.write_hyperparams = write_hyperparams
+        self.wandb_config = {} if wandb_config is None else wandb_config
+
+    def on_train_begin(self, training_info: TrainingInfo, model: Model) -> None:
+        wandb.init(
+            job_type='train',
+            project=self.project,
+            dir=self.model_config.model_output_dir('wandb'),
+            group=self.model_config.name,
+            config=self.wandb_config,
+            name=self.model_config.run_name,
+            resume=training_info.start_epoch_idx > 0,
+            tags=[self.model_config.tag] if self.model_config.tag else []
+        )
+
+        if self.register_model:
+            wandb.watch(model)
+
+        if self.write_hyperparams:
+            path = self.model_config.model_output_dir('wandb', 'vel-config.yaml')
+            with open(path, 'wt') as fp:
+                yaml.dump(self.model_config.render_configuration(), fp)
+                wandb.save(path)
+
+    def on_epoch_end(self, epoch_info):
+        """ Send data to wandb """
+        result = {k.format(): v for k, v in epoch_info.result.items() if k.metric_type == 'scalar'}
+        wandb.log(row=result, step=epoch_info.global_epoch_idx)
+
+
+def create(model_config, register_model: bool = False, wandb_config=None):
+    """ Vel factory function """
+    return WandbStreaming(model_config, register_model=register_model, wandb_config=wandb_config)
diff --git a/vel/train/__init__.py b/vel/train/__init__.py
new file mode 100644
index 00000000..482d91cd
--- /dev/null
+++ b/vel/train/__init__.py
@@ -0,0 +1,2 @@
+from .trainer import Trainer
+from .train_phase import TrainPhase, EmptyTrainPhase
diff --git a/vel/train/phase/__init__.py b/vel/train/phase/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vel/phase/cycle.py b/vel/train/phase/cycle.py
similarity index 79%
rename from vel/phase/cycle.py
rename to vel/train/phase/cycle.py
index 7374bcb1..cb04c409 100644
--- a/vel/phase/cycle.py
+++ b/vel/train/phase/cycle.py
@@ -1,8 +1,10 @@
+import typing
 import numpy as np
 
-import vel.util.intepolate as interp
+import vel.util.interpolate as interp
 
-from vel.api import BatchInfo, EpochInfo, TrainingInfo, Callback, TrainPhase
+from vel.api import BatchInfo, EpochInfo, TrainingInfo, Callback, OptimizedModel
+from vel.train import TrainPhase
 
 
 class CycleCallback(Callback):
@@ -52,12 +54,14 @@ def _init_cycle_dict(self):
 
         return dict_arr, length_arr, start_arr
 
-    def on_batch_begin(self, batch_info: BatchInfo):
+    def on_batch_begin(self, batch_info: BatchInfo, dataset: typing.Optional[str] = None):
         """ Set proper learning rate """
         cycle_length = self.cycle_lengths[batch_info.local_epoch_number - 1]
         cycle_start = self.cycle_starts[batch_info.local_epoch_number - 1]
 
-        numerator = (batch_info.local_epoch_number - cycle_start - 1) * batch_info.batches_per_epoch + batch_info.batch_number
+        numerator = (
+            (batch_info.local_epoch_number - cycle_start - 1) * batch_info.batches_per_epoch + batch_info.batch_number
+        )
         denominator = cycle_length * batch_info.batches_per_epoch
 
         interpolation_number = numerator / denominator
@@ -66,20 +70,21 @@ def on_batch_begin(self, batch_info: BatchInfo):
             lr = self.init_lr
         else:
             if isinstance(self.max_lr, list):
-                lr = [interp.interpolate_single(max_lr, min_lr, interpolation_number, how=self.interpolate) for max_lr, min_lr in zip(self.max_lr, self.min_lr)]
+                lr = [
+                    interp.interpolate_single(max_lr, min_lr, interpolation_number, how=self.interpolate)
+                    for max_lr, min_lr in zip(self.max_lr, self.min_lr)
+                ]
+            elif isinstance(self.max_lr, dict):
+                lr = {
+                    name: interp.interpolate_single(
+                        self.max_lr[name], self.min_lr[name], interpolation_number, how=self.interpolate
+                    )
+                    for name in self.max_lr
+                }
             else:
                 lr = interp.interpolate_single(self.max_lr, self.min_lr, interpolation_number, how=self.interpolate)
 
-        self.set_lr(lr)
-
-    def set_lr(self, lr):
-        """ Set a learning rate for the optimizer """
-        if isinstance(lr, list):
-            for group_lr, param_group in zip(lr, self.optimizer.param_groups):
-                param_group['lr'] = group_lr
-        else:
-            for param_group in self.optimizer.param_groups:
-                param_group['lr'] = lr
+        self.optimizer.set_lr(lr)
 
 
 class CyclePhase(TrainPhase):
@@ -108,7 +113,7 @@ def __init__(self, optimizer_factory, max_lr, min_lr, cycles, cycle_len=1, cycle
         self.freeze = freeze
 
         self._optimizer_instance = None
-        self._source = None
+        self._loader = None
 
         self.special_callback = None
 
@@ -116,11 +121,11 @@ def __init__(self, optimizer_factory, max_lr, min_lr, cycles, cycle_len=1, cycle
     def number_of_epochs(self) -> int:
         return self.epochs
 
-    def set_up_phase(self, training_info, model, source):
+    def set_up_phase(self, training_info, model: OptimizedModel, loader):
         """ Prepare the phase for learning """
         # To parameter groups handles properly filtering parameters that don't require gradient
-        self._optimizer_instance = self.optimizer_factory.instantiate(model)
-        self._source = source
+        self._optimizer_instance = model.create_optimizer(self.optimizer_factory)
+        self._loader = loader
 
         self.special_callback = CycleCallback(
             self._optimizer_instance,
@@ -137,7 +142,7 @@ def epoch_info(self, training_info: TrainingInfo, global_idx: int, local_idx: in
             training_info=training_info,
             global_epoch_idx=global_idx,
             local_epoch_idx=local_idx,
-            batches_per_epoch=self._source.train_iterations_per_epoch(),
+            batches_per_epoch=self._loader.size['train'],
             optimizer=self._optimizer_instance,
             # Add special callback for this epoch
             callbacks=[self.special_callback] + training_info.callbacks
@@ -145,7 +150,7 @@ def epoch_info(self, training_info: TrainingInfo, global_idx: int, local_idx: in
 
     def execute_epoch(self, epoch_info, learner):
         """ Prepare the phase for learning """
-        learner.run_epoch(epoch_info, self._source)
+        learner.run_epoch(epoch_info, self._loader)
 
 
 def create(optimizer, max_lr, min_lr, cycles, cycle_len=1, cycle_mult=1, interpolate='linear', init_lr=0, init_iter=0):
diff --git a/vel/train/phase/freeze.py b/vel/train/phase/freeze.py
new file mode 100644
index 00000000..635b7ebd
--- /dev/null
+++ b/vel/train/phase/freeze.py
@@ -0,0 +1,16 @@
+import vel.train as train
+
+
+class FreezePhase(train.EmptyTrainPhase):
+    """ Freeze the model """
+    def __init__(self, groups=None):
+        self.groups = groups
+
+    def set_up_phase(self, training_info, model, loader):
+        """ Freeze the model """
+        model.freeze(groups=self.groups)
+
+
+def create(groups=None):
+    """ Vel factory function """
+    return FreezePhase(groups)
diff --git a/vel/phase/generic.py b/vel/train/phase/generic.py
similarity index 69%
rename from vel/phase/generic.py
rename to vel/train/phase/generic.py
index 87d0f571..4619a390 100644
--- a/vel/phase/generic.py
+++ b/vel/train/phase/generic.py
@@ -1,4 +1,6 @@
-from vel.api import TrainingInfo, EpochInfo, TrainPhase, Source
+from vel.api import TrainingInfo, EpochInfo, OptimizedModel
+from vel.data.loader import DatasetLoader
+from vel.train import TrainPhase
 
 
 class GenericPhase(TrainPhase):
@@ -10,16 +12,16 @@ def __init__(self, lr, epochs, optimizer_factory):
         self.optimizer_factory = optimizer_factory
 
         self._optimizer_instance = None
-        self._source = None
+        self._loader = None
 
     @property
     def number_of_epochs(self) -> int:
         return self.epochs
 
-    def set_up_phase(self, training_info, model, source: Source):
+    def set_up_phase(self, training_info: TrainingInfo, model: OptimizedModel, loader: DatasetLoader):
         """ Prepare the phase for learning """
-        self._optimizer_instance = self.optimizer_factory.instantiate(model)
-        self._source = source
+        self._optimizer_instance = model.create_optimizer(self.optimizer_factory)
+        self._loader = loader
 
     def epoch_info(self, training_info: TrainingInfo, global_idx: int, local_idx: int) -> EpochInfo:
         """ Create Epoch info """
@@ -27,7 +29,7 @@ def epoch_info(self, training_info: TrainingInfo, global_idx: int, local_idx: in
             training_info=training_info,
             global_epoch_idx=global_idx,
             local_epoch_idx=local_idx,
-            batches_per_epoch=self._source.train_iterations_per_epoch(),
+            batches_per_epoch=self._loader.size['train'],
             optimizer=self._optimizer_instance
         )
 
@@ -36,7 +38,7 @@ def execute_epoch(self, epoch_info, learner):
         for param_group in epoch_info.optimizer.param_groups:
             param_group['lr'] = self.lr
 
-        epoch_result = learner.run_epoch(epoch_info, self._source)
+        epoch_result = learner.run_epoch(epoch_info, self._loader)
 
         return epoch_result
 
diff --git a/vel/phase/unfreeze.py b/vel/train/phase/unfreeze.py
similarity index 56%
rename from vel/phase/unfreeze.py
rename to vel/train/phase/unfreeze.py
index 48b15a0e..8fd40090 100644
--- a/vel/phase/unfreeze.py
+++ b/vel/train/phase/unfreeze.py
@@ -1,10 +1,10 @@
-import vel.api as api
+import vel.train as train
 
 
-class UnfreezePhase(api.EmptyTrainPhase):
+class UnfreezePhase(train.EmptyTrainPhase):
     """ Freeze the model """
 
-    def set_up_phase(self, training_info, model, source):
+    def set_up_phase(self, training_info, model, loader):
         """ Freeze the model """
         model.unfreeze()
 
diff --git a/vel/api/train_phase.py b/vel/train/train_phase.py
similarity index 88%
rename from vel/api/train_phase.py
rename to vel/train/train_phase.py
index d73e7e31..8f7ff84f 100644
--- a/vel/api/train_phase.py
+++ b/vel/train/train_phase.py
@@ -1,6 +1,7 @@
-from torch.optim import Optimizer
+from vel.api import TrainingInfo, EpochInfo, Model, VelOptimizer
+from vel.data.loader import DatasetLoader
 
-from vel.api import TrainingInfo, EpochInfo, Learner, Model, Source
+from .trainer import Trainer
 
 
 class TrainPhase:
@@ -11,7 +12,7 @@ def number_of_epochs(self) -> int:
         """ How many epochs does this phase take """
         raise NotImplementedError
 
-    def set_up_phase(self, training_info: TrainingInfo, model: Model, source: Source) -> Optimizer:
+    def set_up_phase(self, training_info: TrainingInfo, model: Model, loader: DatasetLoader) -> VelOptimizer:
         """ Prepare the phase for learning, returns phase optimizer """
         pass
 
@@ -25,7 +26,7 @@ def epoch_info(self, training_info: TrainingInfo, global_idx: int, local_idx: in
         """ Create Epoch info """
         raise NotImplementedError
 
-    def execute_epoch(self, epoch_info: EpochInfo, learner: Learner):
+    def execute_epoch(self, epoch_info: EpochInfo, trainer: Trainer):
         """
         Execute epoch training.
         """
diff --git a/vel/train/trainer.py b/vel/train/trainer.py
new file mode 100644
index 00000000..2a18266f
--- /dev/null
+++ b/vel/train/trainer.py
@@ -0,0 +1,99 @@
+import sys
+import torch
+import torch.nn
+import tqdm
+
+from vel.api import OptimizedModel, TrainingInfo, EpochInfo, BatchInfo
+from vel.data.loader import DatasetLoader
+
+from vel.util.tensor_util import to_device
+
+
+class Trainer:
+    """ Manages training process of a single model """
+
+    def __init__(self, device: torch.device, model: OptimizedModel):
+        self.device = device
+        self.model: OptimizedModel = model.to(device)
+
+    def metrics(self):
+        """ Return metrics for given learner/model """
+        return self.model.metrics()
+
+    def train(self):
+        """ Set model in the training mode """
+        return self.model.train()
+
+    def eval(self):
+        """ Set model in the evaluation mode """
+        return self.model.eval()
+
+    def number_of_parameters(self):
+        """ Count model parameters """
+        return sum(p.numel() for p in self.model.parameters())
+
+    def initialize_training(self, training_info: TrainingInfo, model_state=None, hidden_state=None):
+        """ Prepare for training """
+        if model_state is None:
+            self.model.reset_weights()
+        else:
+            self.model.load_state_dict(model_state)
+
+    def run_epoch(self, epoch_info: EpochInfo, loader: DatasetLoader):
+        """ Run full epoch of learning """
+        epoch_info.on_epoch_begin()
+
+        lr = epoch_info.optimizer.get_lr()
+        print("|-------- Epoch {:06} Lr={:.6f} ----------|".format(epoch_info.global_epoch_idx, lr))
+
+        self.train_epoch(epoch_info, loader)
+        epoch_info.result_accumulator.freeze_results('train')
+
+        self.validation_epoch(epoch_info, loader)
+        epoch_info.result_accumulator.freeze_results('val')
+
+        epoch_info.on_epoch_end()
+
+    def train_epoch(self, epoch_info, loader: DatasetLoader, interactive=True):
+        """ Run a single training epoch """
+        self.train()
+
+        if interactive:
+            iterator = tqdm.tqdm(loader['train'], desc="Training", unit="iter", file=sys.stdout)
+        else:
+            iterator = loader['train']
+
+        for batch_idx, datapoint in enumerate(iterator):
+            batch_info = BatchInfo(epoch_info, batch_idx)
+            batch_info['datapoint'] = datapoint
+
+            batch_info.on_batch_begin('train')
+            datapoint = to_device(datapoint, self.device)  # Move a data batch into the right device
+            metrics = self.model.optimize(datapoint, batch_info.optimizer)
+            batch_info.update(metrics)
+            batch_info.on_batch_end('train')
+
+            if 'loss' in epoch_info.result_accumulator:
+                iterator.set_postfix(loss=epoch_info.result_accumulator.intermediate_value('loss'))
+
+    def validation_epoch(self, epoch_info, loader: DatasetLoader, interactive=True):
+        """ Run a single evaluation epoch """
+        self.eval()
+
+        if interactive:
+            iterator = tqdm.tqdm(loader['val'], desc="Training", unit="iter", file=sys.stdout)
+        else:
+            iterator = loader['val']
+
+        with torch.no_grad():
+            for batch_idx, datapoint in enumerate(iterator):
+                batch_info = BatchInfo(epoch_info, batch_idx)
+                batch_info['datapoint'] = datapoint
+
+                batch_info.on_batch_begin('val')
+
+                datapoint = to_device(datapoint, self.device)  # Move a data batch into the right device
+                metrics = self.model.validate(datapoint)
+                batch_info.update(metrics)
+
+                batch_info.on_batch_end('val')
diff --git a/vel/util/dataloader.py b/vel/util/dataloader.py
new file mode 100644
index 00000000..0bdbada2
--- /dev/null
+++ b/vel/util/dataloader.py
@@ -0,0 +1,18 @@
+class IteratorDictWrapper:
+    """ Transform dataset loader into a dictionary """
+
+    def __init__(self, iterator, field_mapping):
+        self.iterator = iterator
+        self.field_mapping = field_mapping
+
+    def __iter__(self):
+        return map(self.map_values, iter(self.iterator))
+
+    def __len__(self):
+        return len(self.iterator)
+
+    def map_values(self, item):
+        """ Map iterator values into a dictionary """
+        return {
+            name: getattr(item, argument) for name, argument in self.field_mapping.items()
+        }
diff --git a/vel/util/datastructure.py b/vel/util/datastructure.py
new file mode 100644
index 00000000..455c21e1
--- /dev/null
+++ b/vel/util/datastructure.py
@@ -0,0 +1,21 @@
+import typing
+
+
+def flatten_dict(dictionary: dict, output: typing.Optional[dict] = None, root: str = '') -> dict:
+    """ From a nested dictionary built a flat version, concatenating keys with '.' """
+    if output is None:
+        output = {}
+
+    for key, value in dictionary.items():
+        if isinstance(value, dict):
+            if root:
+                flatten_dict(value, output, f"{root}.{key}")
+            else:
+                flatten_dict(value, output, key)
+        else:
+            if root:
+                output[f"{root}.{key}"] = value
+            else:
+                output[key] = value
+
+    return output
diff --git a/vel/util/intepolate.py b/vel/util/interpolate.py
similarity index 99%
rename from vel/util/intepolate.py
rename to vel/util/interpolate.py
index 29eb72a4..985773c0 100644
--- a/vel/util/intepolate.py
+++ b/vel/util/interpolate.py
@@ -53,4 +53,3 @@ def interpolate_series(start, end, steps, how='linear'):
 def interpolate_single(start, end, coefficient, how='linear'):
     """ Interpolate single value between start and end in given number of steps """
     return INTERP_SINGLE_DICT[how](start, end, coefficient)
-
diff --git a/vel/util/module_util.py b/vel/util/module_util.py
index e08e9eda..4d1ca456 100644
--- a/vel/util/module_util.py
+++ b/vel/util/module_util.py
@@ -38,7 +38,8 @@ def module_broadcast(m, broadcast_fn, *args, **kwargs):
 
 def set_train_mode(module):
     # Only fix ones which we don't want to "train"
-    if hasattr(module, 'running_mean') and (getattr(module, 'bn_freeze', False) or not getattr(module, 'trainable', True)):
+    if hasattr(module, 'running_mean') and (getattr(module, 'bn_freeze', False) or
+                                            not getattr(module, 'trainable', True)):
         module.eval()
     elif getattr(module, 'drop_freeze', False) and hasattr(module, 'p') and ('drop' in type(module).__name__.lower()):
         module.eval()
@@ -80,3 +81,22 @@ def chain_params(p):
 def to_parameter_groups(layer_groups):
     """ Convert from list of layer groups into list of parameter settings for an optimizer """
     return [{'params': chain_params(x)} for x in layer_groups]
+
+
+def module_list_to_param_list(module_list):
+    """ Conver a list of pytorch modules into a list of parameters """
+    return it.chain.from_iterable(m.parameters() for m in module_list)
+
+
+def optimizer_parameter_helper(parameters, parameter_dict):
+    """ Helper function for creating layer group optimizer instances """
+    out_dict = parameter_dict.copy()
+
+    for parameter, value in parameter_dict.items():
+        if isinstance(value, collections.Sequence):
+            for idx, this_value in enumerate(value):
+                parameters[idx][parameter] = this_value
+
+            out_dict[parameter] = value[0]
+
+    return out_dict
diff --git a/vel/math/processes.py b/vel/util/process.py
similarity index 78%
rename from vel/math/processes.py
rename to vel/util/process.py
index cd176b83..923cad44 100644
--- a/vel/math/processes.py
+++ b/vel/util/process.py
@@ -15,7 +15,10 @@ def __init__(self, mu, sigma, theta=.15, dt=1e-2, x0=None):
         self.reset()
 
     def __call__(self):
-        x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)
+        x = (
+            self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt +
+            self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)
+        )
         self.x_prev = x
         return x
 
diff --git a/vel/util/profiling.py b/vel/util/profiling.py
new file mode 100644
index 00000000..0c21a5fb
--- /dev/null
+++ b/vel/util/profiling.py
@@ -0,0 +1,15 @@
+import contextlib
+import time
+
+
+@contextlib.contextmanager
+def timing_context(label=None):
+    """ Measure time of expression as a context """
+    start = time.time()
+    yield
+    end = time.time()
+
+    if label is None:
+        print("Context took {:.2f}s".format(end - start))
+    else:
+        print("{} took {:.2f}s".format(label, end - start))
diff --git a/vel/util/situational.py b/vel/util/situational.py
index d57924f1..4ea7140c 100644
--- a/vel/util/situational.py
+++ b/vel/util/situational.py
@@ -1,8 +1,11 @@
+import gym
 import typing
 
+from vel.api import SizeHints, SizeHint
 
-def process_environment_settings(default_dictionary: dict, settings: typing.Optional[dict]=None,
-                                 presets: typing.Optional[dict]=None):
+
+def process_environment_settings(default_dictionary: dict, settings: typing.Optional[dict] = None,
+                                 presets: typing.Optional[dict] = None):
     """ Process a dictionary of env settings """
     settings = settings if settings is not None else {}
     presets = presets if presets is not None else {}
@@ -26,3 +29,15 @@ def process_environment_settings(default_dictionary: dict, settings: typing.Opti
 
     return result_dict
 
+
+def gym_space_to_size_hint(space: gym.Space) -> SizeHints:
+    """ Convert Gym observation space to size hints """
+    if isinstance(space, gym.spaces.Box):
+        return size_hint_from_shape(space.shape)
+    else:
+        raise NotImplementedError
+
+
+def size_hint_from_shape(shape: typing.Tuple[int]) -> SizeHints:
+    """ Convert tensor shape (without batch dimension) into a size hint """
+    return SizeHints(SizeHint(*([None] + list(shape))))
diff --git a/vel/math/functions.py b/vel/util/stats.py
similarity index 100%
rename from vel/math/functions.py
rename to vel/util/stats.py
diff --git a/vel/util/summary.py b/vel/util/summary.py
deleted file mode 100644
index b5809f39..00000000
--- a/vel/util/summary.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""
-Code based on: https://github.com/sksq96/pytorch-summary/blob/master/torchsummary/torchsummary.py
-"""
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-from collections import OrderedDict
-
-
-def summary(model, input_size):
-    """ Print summary of the model """
-    def register_hook(module):
-        def hook(module, input, output):
-            class_name = str(module.__class__).split('.')[-1].split("'")[0]
-            module_idx = len(summary)
-
-            m_key = '%s-%i' % (class_name, module_idx + 1)
-            summary[m_key] = OrderedDict()
-            summary[m_key]['input_shape'] = list(input[0].size())
-            summary[m_key]['input_shape'][0] = -1
-            if isinstance(output, (list, tuple)):
-                summary[m_key]['output_shape'] = [[-1] + list(o.size())[1:] for o in output]
-            else:
-                summary[m_key]['output_shape'] = list(output.size())
-                summary[m_key]['output_shape'][0] = -1
-
-            params = 0
-            if hasattr(module, 'weight') and hasattr(module.weight, 'size'):
-                params += torch.prod(torch.LongTensor(list(module.weight.size())))
-                summary[m_key]['trainable'] = module.weight.requires_grad
-            if hasattr(module, 'bias') and hasattr(module.bias, 'size'):
-                params += torch.prod(torch.LongTensor(list(module.bias.size())))
-            summary[m_key]['nb_params'] = params
-
-        if (not isinstance(module, nn.Sequential) and
-                not isinstance(module, nn.ModuleList) and
-                not (module == model)):
-            hooks.append(module.register_forward_hook(hook))
-
-    if torch.cuda.is_available():
-        dtype = torch.cuda.FloatTensor
-        model = model.cuda()
-    else:
-        dtype = torch.FloatTensor
-        model = model.cpu()
-
-    # check if there are multiple inputs to the network
-    if isinstance(input_size[0], (list, tuple)):
-        x = [Variable(torch.rand(2, *in_size)).type(dtype) for in_size in input_size]
-    else:
-        x = Variable(torch.rand(2, *input_size)).type(dtype)
-
-    # print(type(x[0]))
-    # create properties
-    summary = OrderedDict()
-    hooks = []
-    # register hook
-    model.apply(register_hook)
-    # make a forward pass
-    # print(x.shape)
-    model(x)
-    # remove these hooks
-    for h in hooks:
-        h.remove()
-
-    print('----------------------------------------------------------------')
-    line_new = '{:>20}  {:>25} {:>15}'.format('Layer (type)', 'Output Shape', 'Param #')
-    print(line_new)
-    print('================================================================')
-    total_params = 0
-    trainable_params = 0
-    for layer in summary:
-        # input_shape, output_shape, trainable, nb_params
-        line_new = '{:>20}  {:>25} {:>15}'.format(layer, str(summary[layer]['output_shape']),
-                                                  '{0:,}'.format(summary[layer]['nb_params']))
-        total_params += summary[layer]['nb_params']
-        if 'trainable' in summary[layer]:
-            if summary[layer]['trainable'] == True:
-                trainable_params += summary[layer]['nb_params']
-        print(line_new)
-    print('================================================================')
-    print('Total params: {0:,}'.format(total_params))
-    print('Trainable params: {0:,}'.format(trainable_params))
-    print('Non-trainable params: {0:,}'.format(total_params - trainable_params))
-    print('----------------------------------------------------------------')
-    # return summary
\ No newline at end of file
diff --git a/vel/util/tensor_accumulator.py b/vel/util/tensor_accumulator.py
deleted file mode 100644
index fd13c2d9..00000000
--- a/vel/util/tensor_accumulator.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import collections
-
-import torch
-
-
-class TensorAccumulator:
-    """ Buffer for tensors that will be stacked together """
-    def __init__(self):
-        self.accumulants = collections.defaultdict(list)
-
-    def add(self, name, tensor):
-        self.accumulants[name].append(tensor)
-
-    def result(self):
-        """ Concatenate accumulated tensors """
-        return {k: torch.stack(v) for k, v in self.accumulants.items()}
diff --git a/vel/util/tensor_util.py b/vel/util/tensor_util.py
index 06e4dd7c..db524bda 100644
--- a/vel/util/tensor_util.py
+++ b/vel/util/tensor_util.py
@@ -1,4 +1,5 @@
 import torch
+import collections
 
 
 def one_hot_encoding(input_tensor, num_labels):
@@ -16,3 +17,36 @@ def merge_first_two_dims(tensor):
     batch_size = shape[0] * shape[1]
     new_shape = tuple([batch_size] + list(shape[2:]))
     return tensor.view(new_shape)
+
+
+def to_device(tensor, device: torch.device):
+    """ Convert tensor-like object to given PyTorch device """
+    if tensor is None:
+        return tensor
+    elif isinstance(tensor, torch.Tensor):
+        return tensor.to(device)
+    elif isinstance(tensor, dict):
+        return {k: to_device(v, device) for k, v in tensor.items()}
+    elif isinstance(tensor, list):
+        return [to_device(v, device) for v in tensor]
+    elif isinstance(tensor, tuple):
+        return tuple(to_device(v, device) for v in tensor)
+    else:
+        raise NotImplementedError
+
+
+class TensorAccumulator:
+    """ Buffer for tensors that will be stacked together """
+    def __init__(self):
+        self.accumulants = collections.defaultdict(list)
+
+    def add(self, name, tensor):
+        if isinstance(tensor, dict):
+            for subname, subtensor in tensor.items():
+                self.add(f"{name}.{subname}", subtensor)
+        else:
+            self.accumulants[name].append(tensor)
+
+    def result(self):
+        """ Concatenate accumulated tensors """
+        return {k: torch.stack(v) for k, v in self.accumulants.items()}
diff --git a/vel/util/visdom.py b/vel/util/visdom.py
index 38877987..6d30369a 100644
--- a/vel/util/visdom.py
+++ b/vel/util/visdom.py
@@ -118,4 +118,3 @@ def visdom_append_metrics(vis, metrics, first_epoch=False):
                     },
                     update=update
                 )
-