Updated 1.dqn for compatability with PyTorch 0.4 and 1.0

[joleeson]

1. Updated for compatibility with latest PyTorch versions. (more thorough than recommendations in Update to run on torch 0.4 #20)

• no longer uses the deprecated "Variable" class
• use of appropriate dtypes
• cpu/gpu agnostic code
• use of tensor.item() for conversion of 0-dimensional tensors to ordinary python numbers

2. Made changes such that the algorithm more closely matches that in Mnih et al. (2015) and other DQN literature:

• linear epsilon decay
• frame stacking
• training frequency is now once every 4 steps in the environment for Atari env
• option of using Huber loss instead of RMS loss in def compute_td_loss()

3. Borrowed monitoring wrapper from OpenAI's Baselines to log progress of training.
4. Modified the wrappers such that it now accommodates stacked frames frame_stack default to False #9 , and outputs them as a LazyFrames object. Axes of the data is appropriately swapped for PyTorch i.e. (no. of channels)x(breadth)x(height)

[colin-leu]

which specific module is this? (can't find a module named logger)