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.
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.
| type | A3C-FF | A3C-LSTM |
|---|---|---|
| GPU | 821 steps per sec | 416 steps per sec |
| CPU | 472 steps per sec | 243 steps per sec |
Score plots of local threads of pong in 24h were like these. (with GTX980Ti)
(70.9 million global steps in 24 hours)
(35.9 million global steps in 24 hours)
Scores are not averaged using global network unlike the original paper.
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.