Playing Atari with DQN
(Working in Progress...)
This project implement DQN paper in Atari 2600 environment. The goal is to train an agent as similar as possible to original paper DQN nature (such as preprocessing, frame skip, other hyperparameters... though there are some modification for practical reasons).
Requirements
gym 0.21.0
ale-py 0.7.3
torch 1.10.0
torchvision 0.11.1
tensorboard 2.3.0
tensorboardX 1.8
Results
Video
| Breakout | Enduro | Space Invaders |
|---|---|---|
 |
 |
 |
Game Score
DQN agent were trained for 10 million frames. Scores for each game are average over 10 episodes.
Run
python test.py --env {env_name} --trained-mode-path {trained_model_path} --record-video to compute average game score and record video (e.g.python test.py --env ALE/Enduro-v5 --trained-model-path trained_model/Enduro.pt --record-video). Trained models are in trained_model directory, but you can train it on your own. Check Train section below. Also, you can add --render-mode human flag to test in interactive environment. Flags are defined in get_test_args function in parse_utils.py.
| Game | DQN (std) |
|---|---|
| Breakout | 97.3 (76.9) |
| Enduro | 275.1 (61.0) |
| Space Invaders | 313.0 (118.4) |
Comparing the average score with original paper, this agent's performance degraded. I believe this is because of small replay memory size. Original paper used a replay memory of 1 million most recent frames, but this agent is trained with 0.35 million replay memory size. This was just due to my PC's memory limit. You can easily modify replay memory size in config.yaml
ROMs
You can download Atari 2600 roms, unzip, place files below in ROMS directory. Note that not all ROMS are supported by ALE. After placing ROM file, you can run ale-import-roms ROMS to check the ROM is supported. To get more infomation about ALE, check repo, blog
In this project used 6 environments (Breakout, Space Invaders, Boxing, Pong, Enduro, Seaquest). For your information, ROMs that I tested are listed below.
List of supported ROM for ALE
- Breakout - Breakaway IV (Paddle) (1978) (Atari, Brad Stewart - Sears) (CX2622 - 6-99813, 49-75107) ~.bin
- Space Invaders (1980) (Atari, Richard Maurer - Sears) (CX2632 - 49-75153) ~.bin
- Boxing - La Boxe (1980) (Activision, Bob Whitehead) (AG-002, CAG-002, AG-002-04) ~.bin
- Pong - Video Olympics - Pong Sports (Paddle) (1977) (Atari, Joe Decuir - Sears) (CX2621 - 99806, 6-99806, 49-75104) ~.bin
- Enduro - Enduro (1983) (Activision, Larry Miller) (AX-026, AX-026-04) ~.bin
- Seaquest - Seaquest (1983) (Activision, Steve Cartwright) (AX-022) ~.bin
Train
After placing ROMs in ROMS directory and verified it is supported by ALE, run python train.py --env {env_name} to train a DQN agent (e.g. python train.py --env ALE/Seaquest-v5). Logs such as Loss, episode reward, exploration factor (epsilon) and others will be logged under log directory (or you can give specify using --log-dir {path_to_dir} flags). Check config.yaml for training configuration. And take a look at get_train_args function in parse_utils.py for flags.