diff --git a/htmresearch/frameworks/pytorch/cnn_sdr.py b/htmresearch/frameworks/pytorch/cnn_sdr.py
index 6d6ac7950..53a527dad 100644
--- a/htmresearch/frameworks/pytorch/cnn_sdr.py
+++ b/htmresearch/frameworks/pytorch/cnn_sdr.py
@@ -41,6 +41,7 @@ def __init__(self,
                outChannels=20,
                k=20,
                kernelSize=5,
+               stride=1,
                kInferenceFactor=1.5,
                boostStrength=1.0,
                useBatchNorm=True,
@@ -57,12 +58,15 @@ def __init__(self,
 
     :param k:
       Number of ON (non-zero) units per iteration in this convolutional layer.
-      The sparsity of this layer will be k / self.outputLength. If k >=
-      self.outputLength, the layer acts as a traditional convolutional layer.
+      The sparsity of this layer will be k / self.outputLength. If k <= 0 or
+      k >= self.outputLength, the layer acts as a traditional convolutional layer.
 
     :param kernelSize:
       Size of the CNN kernel.
 
+    :param stride:
+      stride of the convolution.
+
     :param kInferenceFactor:
       During inference (training=False) we increase k by this factor.
 
@@ -109,10 +113,11 @@ def __init__(self,
     self.kInferenceFactor = kInferenceFactor
     self.kernelSize = kernelSize
     self.imageShape = imageShape
-    self.stride = 1
+    self.stride = stride
     self.padding = 0
 
-    self.cnn = nn.Conv2d(imageShape[0], outChannels, kernel_size=kernelSize)
+    self.cnn = nn.Conv2d(imageShape[0], outChannels, kernel_size=kernelSize,
+                         stride=stride)
 
     self.bn = None
     if useBatchNorm:
@@ -124,6 +129,8 @@ def __init__(self,
     shape = self.outputSize()
     self.maxpoolWidth = int(math.floor(shape[2] / 2.0))
     self.outputLength = int(self.maxpoolWidth * self.maxpoolWidth * outChannels)
+    if k <= 0:
+      self.k = self.outputLength
 
     print("output shape before maxpool:", shape)
     print("maxpool width:", self.maxpoolWidth)
diff --git a/htmresearch/frameworks/pytorch/linear_sdr.py b/htmresearch/frameworks/pytorch/linear_sdr.py
index b52b10596..47140eef2 100644
--- a/htmresearch/frameworks/pytorch/linear_sdr.py
+++ b/htmresearch/frameworks/pytorch/linear_sdr.py
@@ -54,8 +54,8 @@ def __init__(self,
 
     :param k:
       Number of ON units in this layer. The sparsity of this layer will be
-      k / n. If k >= n, the layer acts as a traditional fully connected RELU
-      layer.
+      k / n. If k <= 0 or k >= n, the layer acts as a traditional fully
+      connected RELU layer.
 
     :param kInferenceFactor:
       During inference (training=False) we increase k by this factor.
@@ -81,6 +81,8 @@ def __init__(self,
     self.l1 = nn.Linear(inputFeatures, self.n)
     self.weightSparsity = weightSparsity
     self.learningIterations = 0
+    if self.k <= 0:
+      self.k = self.n
 
     self.bn = None
     if useBatchNorm:
diff --git a/htmresearch/frameworks/pytorch/mnist_sparse_experiment.py b/htmresearch/frameworks/pytorch/mnist_sparse_experiment.py
index 17a199e8e..bb44ef3f3 100644
--- a/htmresearch/frameworks/pytorch/mnist_sparse_experiment.py
+++ b/htmresearch/frameworks/pytorch/mnist_sparse_experiment.py
@@ -137,6 +137,8 @@ def reset(self, params, repetition):
         inputSize=c1_input_shape,
         outChannels=c1_out_channels,
         c_k=c1_k,
+        kernelSize=[5] * len(c1_k),
+        stride=[1] * len(c1_k),
         dropout=params["dropout"],
         n=n,
         k=k,
diff --git a/htmresearch/frameworks/pytorch/sparse_net.py b/htmresearch/frameworks/pytorch/sparse_net.py
index 213dee4d8..1d865f59c 100644
--- a/htmresearch/frameworks/pytorch/sparse_net.py
+++ b/htmresearch/frameworks/pytorch/sparse_net.py
@@ -56,6 +56,8 @@ def __init__(self,
                k=200,
                outChannels=0,
                c_k=0,
+               kernelSize=5,
+               stride=1,
                inputSize=28*28,
                outputSize=10,
                kInferenceFactor=1.0,
@@ -64,7 +66,8 @@ def __init__(self,
                boostStrengthFactor=1.0,
                dropout=0.0,
                useBatchNorm=True,
-               normalizeWeights=False):
+               normalizeWeights=False,
+               useSoftmax=True):
     """
     A network with one or more hidden layers, which can be a sequence of
     k-sparse CNN followed by a sequence of k-sparse linear layer with optional
@@ -100,6 +103,14 @@ def __init__(self,
       layer acts as a traditional convolutional layer.
     :type c_k: int or list[int]
 
+    :param kernelSize:
+      Kernel size to use in each k-sparse convolutional layer.
+    :type kernelSize: int or list[int]
+
+    :param stride:
+      Stride value to use in each k-sparse convolutional layer.
+    :type stride: int or list[int]
+
     :param inputSize:
       If the CNN layer is enable this parameter holds a tuple representing
       (in_channels,height,width). Otherwise it will hold the total
@@ -142,6 +153,9 @@ def __init__(self,
       number of non-zeros instead of the whole input size
     :type normalizeWeights: bool
 
+    :param useSoftmax:
+      If True, use soft max to compute probabilities
+    :type useSoftmax: bool
     """
     super(SparseNet, self).__init__()
 
@@ -149,14 +163,17 @@ def __init__(self,
 
     # Validate CNN sdr params
     if isinstance(inputSize, collections.Sequence):
-      assert(inputSize[1] == inputSize[2],
-             "sparseCNN only supports square images")
+      assert inputSize[1] == inputSize[2], "sparseCNN only supports square images"
 
     if type(outChannels) is not list:
       outChannels = [outChannels]
     if type(c_k) is not list:
       c_k = [c_k]
     assert(len(outChannels) == len(c_k))
+    if type(kernelSize) is not list:
+      kernelSize = [kernelSize]
+    if type(stride) is not list:
+      stride = [stride]
 
     # Validate linear sdr params
     if type(n) is not list:
@@ -176,9 +193,11 @@ def __init__(self,
     self.weightSparsity = weightSparsity   # Pct of weights that are non-zero
     self.boostStrengthFactor = boostStrengthFactor
     self.boostStrength = boostStrength
-    self.kernelSize = 5
+    self.kernelSize = kernelSize
+    self.stride = stride
     self.learningIterations = 0
 
+
     inputFeatures = inputSize
     cnnSdr = nn.Sequential()
     # CNN Layers
@@ -187,7 +206,8 @@ def __init__(self,
         module = CNNSDR2d(imageShape=inputFeatures,
                           outChannels=outChannels[i],
                           k=c_k[i],
-                          kernelSize=self.kernelSize,
+                          kernelSize=self.kernelSize[i],
+                          stride=self.stride[i],
                           kInferenceFactor=kInferenceFactor,
                           boostStrength=boostStrength,
                           useBatchNorm=useBatchNorm,
@@ -228,8 +248,13 @@ def __init__(self,
         inputFeatures = n[i]
 
     # Add one fully connected layer after all hidden layers
-    self.fc = nn.Linear(self.n[-1], outputSize)
-    self.softmax = nn.LogSoftmax(dim=1)
+    self.fc = nn.Linear(inputFeatures, outputSize)
+
+    # Use softmax to compute probabilities
+    if useSoftmax:
+      self.softmax = nn.LogSoftmax(dim=1)
+    else:
+      self.softmax = None
 
 
   def postEpoch(self):
@@ -260,7 +285,9 @@ def forward(self, x):
     x = self.flatten(x)
     x = self.linearSdr(x)
     x = self.fc(x)
-    x = self.softmax(x)
+
+    if self.softmax is not None:
+      x = self.softmax(x)
 
     if self.training:
       batchSize = x.shape[0]
diff --git a/htmresearch/frameworks/pytorch/sparse_speech_experiment.py b/htmresearch/frameworks/pytorch/sparse_speech_experiment.py
index 5ac2c52eb..740d6ebfe 100644
--- a/htmresearch/frameworks/pytorch/sparse_speech_experiment.py
+++ b/htmresearch/frameworks/pytorch/sparse_speech_experiment.py
@@ -113,6 +113,8 @@ def reset(self, params, repetition):
         outputSize=len(self.train_loader.dataset.classes),
         outChannels=c1_out_channels,
         c_k=c1_k,
+        kernelSize=[5] * len(c1_k),
+        stride=[1] * len(c1_k),
         dropout=params["dropout"],
         n=n,
         k=k,
diff --git a/htmresearch/frameworks/rl/__init__.py b/htmresearch/frameworks/rl/__init__.py
new file mode 100644
index 000000000..fc6dc490a
--- /dev/null
+++ b/htmresearch/frameworks/rl/__init__.py
@@ -0,0 +1,21 @@
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2019, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero Public License for more details.
+#
+# You should have received a copy of the GNU Affero Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+from .dqn import DQN
diff --git a/htmresearch/frameworks/rl/dqn.py b/htmresearch/frameworks/rl/dqn.py
new file mode 100644
index 000000000..932c6e093
--- /dev/null
+++ b/htmresearch/frameworks/rl/dqn.py
@@ -0,0 +1,175 @@
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2019, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero Public License for more details.
+#
+# You should have received a copy of the GNU Affero Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+import collections
+import random
+import copy
+
+class DQN(object):
+  """
+  Implements **DQN algorithm** described in https://arxiv.org/pdf/1312.5602.pdf
+  """
+
+
+  def __init__(self, actions, network,
+               eps_start=1.0, eps_end=0.01, eps_decay=0.9995,
+               learning_rate=0.0001, gamma=0.99, tau=1.0, target_update=1000,
+               batch_size=32, min_steps=10000, replay_size=10000):
+    """
+    :param actions: Number of possible actions
+    :param network: Neural neural network to use as a Q function approximator
+    :param eps_start: e-greedy exploration start epsilon
+    :param eps_end: e-greedy exploration end epsilon
+    :param eps_decay: e-greedy exploration decay
+    :param learning_rate: optimizer learning rate
+    :param gamma: future returns discount
+    :param tau: soft update smoothing coefficient. Use 1.0 for hard target update
+    :param target_update: target update interval
+    :param batch_size: batch size
+    :param min_steps: min number of experiences in replay buffer before learning
+    :param replay_size: replay memory size
+    """
+
+    self.actions = actions
+
+    # e-greedy parameters
+    self.eps = eps_start
+    self.eps_end = eps_end
+    self.eps_decay = eps_decay
+
+    # Experience replay memory: e = (s, a, r, s', done)
+    self.replay = collections.deque(maxlen=replay_size)
+    self.min_steps = min_steps
+    self.batch_size = batch_size
+
+    # Initialize Local and Target networks
+    self.local = network
+    self.target = copy.deepcopy(self.local)
+    self.device = next(network.parameters()).device
+
+    # Optimizations
+    self.optimizer = optim.Adam(self.local.parameters(), lr=learning_rate)
+    self.gamma = gamma
+    self.tau = tau
+    self.target_update = target_update
+    self.steps = 0
+
+
+  def select_action(self, state):
+    """
+    Select the best action for the given state using e-greedy exploration to
+    minimize overfitting
+
+    :return: tuple(action, value)
+    """
+    value = 0
+    if self.steps < self.min_steps:
+      action = np.random.randint(self.actions)
+    else:
+      self.eps = max(self.eps_end, self.eps * self.eps_decay)
+      if random.random() < self.eps:
+        action = np.random.randint(self.actions)
+      else:
+        self.local.eval()
+        with torch.no_grad():
+          state = torch.tensor(state, device=self.device, dtype=torch.float).unsqueeze(0)
+          Q = self.local(state)
+          value, action = torch.max(Q, 1)
+
+    return int(action), float(value)
+
+
+  def hard_target_update(self):
+    """
+    Update model parameters every 'target_update' time steps
+    See https://arxiv.org/abs/1312.5602
+    :param target_update: target update interval
+    """
+    if self.steps % self.target_update == 0:
+      self.target.load_state_dict(self.local.state_dict())
+
+
+  def soft_target_update(self):
+    """
+    Soft update model parameters:
+
+    .. math::
+      \\theta_target = \\tau \\times \\theta_local + (1 - \\tau) \\times \\theta_target ,
+      with \\tau \\ll 1
+
+    See https://arxiv.org/pdf/1509.02971.pdf
+    """
+    for target_param, local_param in zip(self.target.parameters(), self.local.parameters()):
+      target_param.data.copy_(self.tau * local_param.data + (1.0 - self.tau) * target_param.data)
+
+
+  def learn(self, state, action, reward, next_state, done):
+    """
+    Update replay memory and learn from a batch of random experiences sampled
+    from the replay buffer
+    :return: optimization loss if enough experiences are available, None otherwise
+    """
+    self.steps += 1
+    self.replay.append((state, action, reward, next_state, done))
+    if self.steps > self.min_steps and len(self.replay) > self.batch_size:
+      batch = random.sample(self.replay, self.batch_size)
+      return self.optimize(batch)
+
+    return None
+
+
+  def optimize(self, batch):
+    state, action, reward, next_state, done = map(
+      lambda x: torch.tensor(x, device=self.device, dtype=torch.float), zip(*batch))
+
+    # Get target values
+    self.target.eval()
+    with torch.no_grad():
+      target_next = self.target(next_state)
+      target_next = torch.max(target_next, dim=1, keepdim=True)[0]
+    Q_target = reward + self.gamma * target_next * (1 - done)
+
+    # Get actual values
+    self.local.train()
+    Q_local = self.local(state)
+    Q_local = Q_local.index_select(1, action.long())
+
+    # Compute Huber loss
+    loss = F.smooth_l1_loss(Q_local, Q_target)
+
+    self.optimizer.zero_grad()
+    loss.backward()
+    for param in self.local.parameters():
+      param.grad.data.clamp_(-1, 1)
+
+    self.optimizer.step()
+
+    # Update target model
+    if self.tau < 1:
+      self.soft_target_update()
+    else:
+      self.hard_target_update()
+
+    return loss.item()
+
diff --git a/htmresearch/frameworks/rl/openai_utils.py b/htmresearch/frameworks/rl/openai_utils.py
new file mode 100644
index 000000000..c42c6af5e
--- /dev/null
+++ b/htmresearch/frameworks/rl/openai_utils.py
@@ -0,0 +1,203 @@
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2019, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero Public License for more details.
+#
+# You should have received a copy of the GNU Affero Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+"""
+OpenAI Atari utilities extracted from https://github.com/openai/baselines
+
+see https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
+"""
+from collections import deque
+
+import gym
+import numpy as np
+from torchvision import transforms
+
+
+
+class NoopResetEnv(gym.Wrapper):
+  def __init__(self, env, noop_max=30):
+    """Sample initial states by taking random number of no-ops on reset.
+    No-op is assumed to be action 0.
+    """
+    gym.Wrapper.__init__(self, env)
+    self.noop_max = noop_max
+    self.override_num_noops = None
+    self.noop_action = 0
+    assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
+
+
+  def reset(self, **kwargs):
+    """ Do no-op action for a number of steps in [1, noop_max]."""
+    self.env.reset(**kwargs)
+    if self.override_num_noops is not None:
+      noops = self.override_num_noops
+    else:
+      noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)  # pylint: disable=E1101
+    assert noops > 0
+    obs = None
+    for _ in range(noops):
+      obs, _, done, _ = self.env.step(self.noop_action)
+      if done:
+        obs = self.env.reset(**kwargs)
+    return obs
+
+
+  def step(self, ac):
+    return self.env.step(ac)
+
+
+
+class FireResetEnv(gym.Wrapper):
+  def __init__(self, env):
+    """Take action on reset for environments that are fixed until firing."""
+    gym.Wrapper.__init__(self, env)
+    assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
+    assert len(env.unwrapped.get_action_meanings()) >= 3
+
+
+  def reset(self, **kwargs):
+    self.env.reset(**kwargs)
+    obs, _, done, _ = self.env.step(1)
+    if done:
+      self.env.reset(**kwargs)
+    obs, _, done, _ = self.env.step(2)
+    if done:
+      self.env.reset(**kwargs)
+    return obs
+
+
+  def step(self, ac):
+    return self.env.step(ac)
+
+
+
+class EpisodicLifeEnv(gym.Wrapper):
+  def __init__(self, env):
+    """Make end-of-life == end-of-episode, but only reset on true game over.
+    Done by DeepMind for the DQN and co. since it helps value estimation.
+    """
+    gym.Wrapper.__init__(self, env)
+    self.lives = 0
+    self.was_real_done = True
+
+
+  def step(self, action):
+    obs, reward, done, info = self.env.step(action)
+    self.was_real_done = done
+    # check current lives, make loss of life terminal,
+    # then update lives to handle bonus lives
+    lives = self.env.unwrapped.ale.lives()
+    if lives < self.lives and lives > 0:
+      # for Qbert sometimes we stay in lives == 0 condition for a few frames
+      # so it's important to keep lives > 0, so that we only reset once
+      # the environment advertises done.
+      done = True
+    self.lives = lives
+    return obs, reward, done, info
+
+
+  def reset(self, **kwargs):
+    """Reset only when lives are exhausted.
+    This way all states are still reachable even though lives are episodic,
+    and the learner need not know about any of this behind-the-scenes.
+    """
+    if self.was_real_done:
+      obs = self.env.reset(**kwargs)
+    else:
+      # no-op step to advance from terminal/lost life state
+      obs, _, _, _ = self.env.step(0)
+    self.lives = self.env.unwrapped.ale.lives()
+    return obs
+
+
+
+class ClipRewardEnv(gym.RewardWrapper):
+  def __init__(self, env):
+    gym.RewardWrapper.__init__(self, env)
+
+
+  def reward(self, reward):
+    """Bin reward to {+1, 0, -1} by its sign."""
+    return np.sign(reward)
+
+
+
+class WarpFrame(gym.ObservationWrapper):
+  """
+  Warp frames to 84x84 as done in the Nature paper and later work.
+  """
+
+
+  def __init__(self, env):
+    gym.ObservationWrapper.__init__(self, env)
+    self.transform = transforms.Compose([transforms.ToPILImage(),
+                                         transforms.Grayscale(),
+                                         transforms.Resize(size=(110, 84)),
+                                         transforms.CenterCrop(84),
+                                         transforms.ToTensor()])
+
+
+  def observation(self, frame):
+    return self.transform(frame)
+
+
+
+class FrameStack(gym.Wrapper):
+  def __init__(self, env, k):
+    """
+    Stack k last frames.
+    """
+    gym.Wrapper.__init__(self, env)
+    self.frames = deque(maxlen=k)
+    self.k = k
+
+
+  def reset(self):
+    reset = self.env.reset()
+    self.frames.extend([reset] * self.k)
+    return np.concatenate(self.frames, axis=0)
+
+
+  def step(self, action):
+    obs, reward, done, info = self.env.step(action)
+    self.frames.append(obs)
+    obs = np.concatenate(self.frames, axis=0)
+    return obs, reward, done, info
+
+
+
+def create_atari_environment(name):
+  """
+  Create OpenAI Gym atari environment
+  :param name: name of  the atari game
+  :return: Preconfigured environment suitable for atari games
+  """
+  env = gym.make(name)
+  env = NoopResetEnv(env, noop_max=30)
+  env = EpisodicLifeEnv(env)
+  env = ClipRewardEnv(env)
+
+  action_names = env.unwrapped.get_action_meanings()
+  if 'FIRE' in action_names:
+    env = FireResetEnv(env)
+
+  env = WarpFrame(env)
+  env = FrameStack(env, k=4)
+  return env
diff --git a/projects/sparse_rl/Plot_SparseRL_Results.ipynb b/projects/sparse_rl/Plot_SparseRL_Results.ipynb
new file mode 100644
index 000000000..62ae75789
--- /dev/null
+++ b/projects/sparse_rl/Plot_SparseRL_Results.ipynb
@@ -0,0 +1,593 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Run Experiment\n",
+    "Uncomment the following commands to run the experiments. Keep in mind that each experiment takes about 4h on a `p2.xlarge` machine. I do not recommend running from your laptop,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !python sparse_dqn.py --sparse --timesteps 100000 --results_path results.sparse\n",
+    "# !python sparse_dqn.py --dense --timesteps 100000 --results_path results.dense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sparse = pd.read_csv('results.sparse/SeaquestDeterministic-v4.train.csv')\n",
+    "dense = pd.read_csv('results.dense/SeaquestDeterministic-v4.train.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x121dd0b00>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(sparse['values'])\n",
+    "fig.suptitle('Value Function (Sparse)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    100000.000000\n",
+       "mean          1.833002\n",
+       "std           0.772533\n",
+       "min           0.000000\n",
+       "25%           1.684198\n",
+       "50%           2.035855\n",
+       "75%           2.323535\n",
+       "max           2.727120\n",
+       "Name: values, dtype: float64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse['values'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x110216dd8>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(dense['values'])\n",
+    "fig.suptitle('Value Function (Dense)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    100000.000000\n",
+       "mean          1.996066\n",
+       "std           0.918295\n",
+       "min           0.000000\n",
+       "25%           1.620196\n",
+       "50%           2.272680\n",
+       "75%           2.728924\n",
+       "max           3.155729\n",
+       "Name: values, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dense['values'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x11b599358>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(sparse['values'].rolling(10000).mean(), label='sparse')\n",
+    "ax.plot(dense['values'].rolling(10000).mean(), label='dense')\n",
+    "fig.suptitle('Value Function (Dense vs Sparse)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x11dd463c8>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(sparse['score'])\n",
+    "fig.suptitle('Score (Sparse)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    100000.000000\n",
+       "mean         11.631200\n",
+       "std          20.890561\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%          20.000000\n",
+       "max         140.000000\n",
+       "Name: score, dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse['score'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x11ddcaf98>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(dense['score'])\n",
+    "fig.suptitle('Score (Dense)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    100000.000000\n",
+       "mean         11.190800\n",
+       "std          21.792539\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           0.000000\n",
+       "75%          20.000000\n",
+       "max         180.000000\n",
+       "Name: score, dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dense['score'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x11de18828>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(sparse['score'].rolling(10000).mean(), label='sparse')\n",
+    "ax.plot(dense['score'].rolling(10000).mean(), label='dense')\n",
+    "fig.suptitle('Score (Dense vs Sparse)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x12200b278>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(sparse['losses'])\n",
+    "fig.suptitle('Loss (Sparse)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    90000.000000\n",
+       "mean         0.190537\n",
+       "std          0.333389\n",
+       "min          0.000006\n",
+       "25%          0.000168\n",
+       "50%          0.000369\n",
+       "75%          0.608106\n",
+       "max          2.493294\n",
+       "Name: losses, dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sparse['losses'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x125610a58>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(dense['losses'])\n",
+    "fig.suptitle('Loss (Dense)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    90000.000000\n",
+       "mean         0.190884\n",
+       "std          0.332980\n",
+       "min          0.000012\n",
+       "25%          0.000269\n",
+       "50%          0.000591\n",
+       "75%          0.210502\n",
+       "max          3.044820\n",
+       "Name: losses, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dense['losses'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x12a115b70>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20,5))\n",
+    "ax.plot(sparse['losses'].rolling(10000).mean(), label='sparse')\n",
+    "ax.plot(dense['losses'].rolling(10000).mean(), label='dense')\n",
+    "fig.suptitle('Loss (Dense vs Sparse)', fontsize=16)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/projects/sparse_rl/requirements.txt b/projects/sparse_rl/requirements.txt
new file mode 100644
index 000000000..558c2c2bc
--- /dev/null
+++ b/projects/sparse_rl/requirements.txt
@@ -0,0 +1,5 @@
+gym[atari]
+torch
+torchvision
+matplotlib
+tqdm
\ No newline at end of file
diff --git a/projects/sparse_rl/results.dense/SeaquestDeterministic-v4.train.csv b/projects/sparse_rl/results.dense/SeaquestDeterministic-v4.train.csv
new file mode 100644
index 000000000..f74d09700
--- /dev/null
+++ b/projects/sparse_rl/results.dense/SeaquestDeterministic-v4.train.csv
@@ -0,0 +1,100001 @@
+episode,score,losses,values
+0,0.0,,0.0
+0,0.0,,0.0
+0,0.0,,0.0
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+0,0.0,,0.0
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+0,0.0,,0.0
+0,0.0,,0.0
+0,0.0,,0.0
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index 000000000..4b2089023
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diff --git a/projects/sparse_rl/sparse_dqn.py b/projects/sparse_rl/sparse_dqn.py
new file mode 100644
index 000000000..7a9cf3731
--- /dev/null
+++ b/projects/sparse_rl/sparse_dqn.py
@@ -0,0 +1,221 @@
+# ----------------------------------------------------------------------
+# Numenta Platform for Intelligent Computing (NuPIC)
+# Copyright (C) 2019, Numenta, Inc.  Unless you have an agreement
+# with Numenta, Inc., for a separate license for this software code, the
+# following terms and conditions apply:
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero Public License version 3 as
+# published by the Free Software Foundation.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero Public License for more details.
+#
+# You should have received a copy of the GNU Affero Public License
+# along with this program.  If not, see http://www.gnu.org/licenses.
+#
+# http://numenta.org/licenses/
+# ----------------------------------------------------------------------
+
+from __future__ import print_function
+
+import argparse
+import csv
+import os
+import random
+
+import numpy as np
+import torch
+import torch.nn as nn
+import tqdm
+
+from htmresearch.frameworks.pytorch.sparse_net import SparseNet
+from htmresearch.frameworks.rl import DQN
+from htmresearch.frameworks.rl.openai_utils import create_atari_environment
+
+SEED = 42
+SPARSE = True
+NUM_TIMESTEPS = int(1e6)
+ENVIRONMENT = "SeaquestDeterministic-v4"
+RESULTS_PATH = "results"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+
+class Flatten(nn.Module):
+  """
+  Flatten input retaining batch dimension
+  """
+
+
+  def forward(self, x):
+    return x.view(x.size(0), -1)
+
+
+
+class DenseQ(nn.Module):
+  """
+  Neural Network described in https://arxiv.org/pdf/1312.5602.pdf
+  """
+
+
+  def __init__(self, actions):
+    super(DenseQ, self).__init__()
+
+    inputShape = (4, 84, 84)
+    self.model = nn.Sequential(
+      nn.Conv2d(in_channels=inputShape[0], out_channels=16, kernel_size=8,
+                stride=4),
+      nn.ReLU(),
+      nn.Conv2d(in_channels=16, out_channels=32, kernel_size=4, stride=2),
+      nn.ReLU(),
+      Flatten(),
+      nn.Linear(in_features=32 * 9 * 9, out_features=256),
+      nn.LayerNorm(256),
+      nn.ReLU(),
+      nn.Linear(in_features=256, out_features=actions),
+    )
+
+
+  def forward(self, x):
+    return self.model(x)
+
+
+
+class SparseQ(nn.Module):
+  """
+  Sparse version of DenseQ network
+  """
+
+
+  def __init__(self, actions):
+    super(SparseQ, self).__init__()
+
+    self.model = SparseNet(inputSize=(4, 84, 84),
+                           outChannels=[16, 32],
+                           kernelSize=[8, 4],
+                           stride=[4, 2],
+                           n=256,
+                           outputSize=actions,
+                           c_k=[160, 320], k=40, weightSparsity=0.3,
+                           useBatchNorm=True,
+                           useSoftmax=False)
+
+
+  def forward(self, x):
+    return self.model(x)
+
+
+
+def run(env, model, learn, numSteps, render, resultsPath, checkpoint=None):
+  """
+  Main RL loop where the agent acts on the environment based on the states
+  :param environment: Open AI Gym environment
+  :param model: Neural neural network model to use as a Q function approximator
+  :param learn: Controls learning. Set to False when using a pre-trained model
+  :param numSteps: Total number of steps/frames to run
+  :param render: Whether or not to render the environment as it runs
+  :param resultsPath: Where to store the results
+  :param checkpoint: How often should the model be saved. None for never
+  """
+
+  path = os.path.join(resultsPath, "{0}.{1}.csv".format(
+    env.spec.id, "train" if learn else "eval"))
+  with open(path, "w") as f:
+    log = csv.writer(f)
+    log.writerow(["episode", "score", "losses", "values"])
+    score = 0
+    episode = 0
+    loss = 0
+
+    # Train for numSteps
+    agent = DQN(env.action_space.n, model)
+    state = env.reset()
+    progess_bar = tqdm.trange(numSteps, desc="t")
+    for t in progess_bar:
+      if render:
+        env.render()
+
+      action, value = agent.select_action(state)
+      next_state, reward, done, info = env.step(action)
+      if learn:
+        loss = agent.learn(state, action, reward, next_state, done)
+      state = next_state
+
+      # Collect stats
+      score += reward
+      log.writerow([episode, score, loss, value])
+
+      # Save checkpoint
+      if checkpoint is not None and t % checkpoint == 0:
+        path = os.path.join(resultsPath, "{0}.checkpoint.{1:08d}".format(env.spec.id, t))
+        torch.save(model, path)
+
+      if value > 0:
+        progess_bar.set_description("{0}:{1}:{2}".format(episode, score, value))
+
+      if done:
+        episode += 1
+        state = env.reset()
+        score = 0
+
+  if learn:
+    path = os.path.join(resultsPath, "{0}.pt".format(env.spec.id))
+    torch.save(model, path)
+
+
+
+def main(opts):
+  try:
+    os.mkdir(opts.results_path)
+  except OSError:
+    pass
+
+  random.seed(opts.seed)
+  np.random.seed(opts.seed)
+  torch.manual_seed(opts.seed)
+
+  # Create environment
+  env = create_atari_environment(opts.env)
+  env.seed(opts.seed)
+
+  # Look for trained model
+  path = os.path.join(opts.results_path, "{0}.pt".format(env.spec.id))
+  if os.path.exists(path):
+    learn = False
+    model = torch.load(path, map_location=device)
+  else:
+    learn = True
+    if opts.sparse:
+      model = SparseQ(actions=env.action_space.n).to(device)
+    else:
+      model = DenseQ(actions=env.action_space.n).to(device)
+
+  run(env=env, model=model, learn=learn, numSteps=opts.timesteps,
+      render=opts.render, resultsPath=opts.results_path)
+
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser(
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument("--env", default=ENVIRONMENT, metavar="STRING",
+                      type=str, help="OpenAI Gym atari environment name")
+  parser.add_argument("--sparse", action="store_true", default=SPARSE)
+  parser.add_argument("--dense", action="store_false", dest="sparse")
+  parser.add_argument("--timesteps", default=NUM_TIMESTEPS, metavar="N",
+                      type=int, help="Number of time steps to run")
+  parser.add_argument("--results_path", default=RESULTS_PATH, metavar="PATH",
+                      type=str, help="Path to store results")
+  parser.add_argument("--seed", default=SEED, type=int,
+                      help="Random seed to use")
+  parser.add_argument("--render", action="store_true", default=False,
+                      help="Render environment UI")
+
+  opts = parser.parse_args()
+  for arg in vars(opts):
+    print(arg, "=", getattr(opts, arg))
+
+  main(opts)
diff --git a/requirements.txt b/requirements.txt
index 172dc1e1f..e3cf84739 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,3 +22,5 @@ scikit-image>=0.14.1
 nupic
 htmresearch_core
 librosa>=0.6.2
+gym>=0.10.11
+atari_py
\ No newline at end of file