Asynchronous deep reinforcement learning

An attempt to repdroduce Google Deep Mind's paper "Asynchronous Methods for Deep Reinforcement Learning."

http://arxiv.org/abs/1602.01783

Asynchronous Advantage Actor-Critic (A3C) method for playing "Atari Pong" is implemented with TensorFlow. Both A3C-FF and A3C-LSTM are implemented.

Learning result movment after 26 hours (A3C-FF) is like this.

Any advice or suggestion is strongly welcomed in issues thread.

#1

First we need to build multi thread ready version of Arcade Learning Enviroment. I made some modification to it to run it on multi thread enviroment.

$ git clone https://github.com/miyosuda/Arcade-Learning-Environment.git
$ cd Arcade-Learning-Environment
$ cmake -DUSE_SDL=ON -DUSE_RLGLUE=OFF -DBUILD_EXAMPLES=OFF .
$ make -j 4

$ pip install .

I recommend to install it on VirtualEnv environment.

To train,

$python a3c.py

To display the result with game play,

$python a3c_disp.py

To enable gpu, change "USE_GPU" flag in "constants.py".

When running with 8 parallel game environemts, speeds of GPU (GTX980Ti) and CPU(Core i7 6700) were like this. (Recorded with LOCAL_T_MAX=20 setting.)

Score plots of local threads of pong were like these. (with GTX980Ti)

Scores are not averaged using global network unlike the original paper.

• TensorFlow r1.0
• numpy
• cv2
• matplotlib

This project uses setting written in muupan's wiki [muuupan/async-rl] (https://github.com/muupan/async-rl/wiki)

• @aravindsrinivas for providing information for some of the hyper parameters.