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Code and models for Atari games in gym

Implemented Multi-GPU version of the A3C algorithm in Asynchronous Methods for Deep Reinforcement Learning.

Results of the same code trained on 47 different Atari games were uploaded on OpenAI Gym. You can see them in my gym page. Most of them are the best reproducible results on gym.

To train on an Atari game:

./train-atari.py --env Breakout-v0 --gpu 0

In each iteration it trains on a batch of 128 new states. The speed is about 6~10 iterations/s on 1 GPU plus 12+ CPU cores. With 2 TitanX + 20+ CPU cores, by setting SIMULATOR_PROC=240, PREDICT_BATCH_SIZE=30, PREDICTOR_THREAD_PER_GPU=6, it can improve to 16 it/s (2K images/s). Note that the network architecture is larger than what's used in the original paper.

The pre-trained models are all trained with 4 GPUs for about 2 days. But on simple games like Breakout, you can get good performance within several hours. Also note that multi-GPU doesn't give you obvious speedup here, because the bottleneck in this implementation is not computation but data.

Some practicical notes:

  1. Prefer Python 3.
  2. Occasionally, processes may not get terminated completely. It is suggested to use systemd-run to run any multiprocess Python program to get a cgroup dedicated for the task.
  3. Training with a significant slower speed (e.g. on CPU) will result in very bad score, probably because of async issues.

To test a model:

Download models from model zoo.

Watch the agent play: ./train-atari.py --task play --env Breakout-v0 --load Breakout-v0.npy

Generate gym submissions: ./train-atari.py --task gen_submit --load Breakout-v0.npy --env Breakout-v0 --output output_dir

Models are available for the following atari environments (click to watch videos of my agent):

AirRaid Alien Amidar Assault
Asterix Asteroids Atlantis BankHeist
BattleZone BeamRider Berzerk Breakout
Carnival Centipede ChopperCommand CrazyClimber
DemonAttack DoubleDunk ElevatorAction FishingDerby
Frostbite Gopher Gravitar IceHockey
Jamesbond JourneyEscape Kangaroo Krull
KungFuMaster MsPacman NameThisGame Phoenix
Pong Pooyan Qbert Riverraid
RoadRunner Robotank Seaquest SpaceInvaders
StarGunner Tennis Tutankham UpNDown
VideoPinball WizardOfWor Zaxxon

Note that atari game settings in gym (AtariGames-v0) are quite different from DeepMind papers, so the scores are not comparable. The most notable differences are:

  • Each action is randomly repeated 2~4 times.
  • Inputs are RGB instead of greyscale.
  • An episode is limited to 10000 steps.
  • Lost of live is not end of episode.

Also see the DQN implementation here