RL with Atari

Install

First, install gym and atari environments. You may need to install other dependencies depending on your system.

pip install gym

and then install atari with one of the following commands

pip install "gym[atari]"
pip install gym[atari]

We also require you to use a version greater than 1 for Tensorflow.

Environment

Pong-v0

• We play against a decent AI player.
• One player wins if the ball pass through the other player and gets reward +1 else -1.
• Episode is over when one of the player reaches 21 wins
• final score is between -21 or +21 (lost all or won all)

# action = int in [0, 6)
# state  = (210, 160, 3) array
# reward = 0 during the game, 1 if we win, -1 else

We use a modified env where the dimension of the input is reduced to

# state = (80, 80, 1)

with downsampling and greyscale.

Training

Once done with implementing q2_linear.py (setup of the tensorflow necessary op) and q3_nature make sure you test your implementation by launching python q2_linear.py and python q3_nature.py that will run your code on the Test environment.

You can launch the training of DeepMind's DQN on pong with

python q5_train_atari_nature.py

The default config file should be sufficient to reach good performance after 5 million steps.

Training tips: (1) The starter code writes summaries of a bunch of useful variables that can help you monitor the training process. You can monitor your training with Tensorboard by doing, on Azure

tensorboard --logdir=results

and then connect to ip-of-you-machine:6006

(2) You can use ‘screen’ to manage windows on VM and to retrieve running programs. Before training DQN on Atari games, run

screen 

then run

python q5_train_atari_nature.py

By using Screen, programs continue to run when their window is currently not visible and even when the whole screen session is detached from the users terminal.

To retrieve your running program on VM, simply type

screen -r

which will recover the detached window.

P.S. Please use Python 2.7.X to develop your code (NOT Python 3). The final grading will be run on Linux servers and will be compatible with all Python 2.7.X versions.

Credits Assignment code written by Guillaume Genthial and Shuhui Qu.