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deep reinforcement learning on Atari games, homework assignment for IE 534 Fall 2019 at UIUC, taught by Prof. Justin Sirignano

Home Page: https://courses.engr.illinois.edu/ie534/fa2019/

License: GNU General Public License v3.0

HTML 66.92% Jupyter Notebook 27.80% Python 5.03% Shell 0.25%

ie534_rl_hw's Introduction

IE 534 Assignment: Reinforcement Learning

Getting Started

You can either:

git clone https://github.com/mikuhatsune/ie534_rl_hw.git

Please follow instructions in the Jupyter notebook rl.ipynb.

An example of finished homework is in example_solution/rl.ipynb and example_solution/rl.pdf.

Example training logs example_solution/log_breakout_dqn.txt, and example_solution/log_breakout_a2c.txt. Format:

iter: iteration
n_ep: number of episodes (games played)
ep_len: running averaged episode length
ep_rew: running averaged episode clipped reward
raw_ep_rew: running averaged raw episode reward (actual raw game score)
env_step: number of environment simulation steps
time, rem: time passed, estimated time remain

iter    500 |loss   0.00 |n_ep    28 |ep_len   31.3 |ep_rew  -0.22 |raw_ep_rew   1.76 |env_step   1000 |time 00:04 rem 281:49

Important

Run these commands once to make BlueWaters happy (install a newer version of gym):

module load python/2.0.0
pip install gym[atari]==0.14 --user

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