### Code and models for Atari games in gym

Implemented A3C in [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783).

Results of the same code trained on 47 different Atari games were uploaded on OpenAI Gym.
You can see them in [my gym page](https://gym.openai.com/users/ppwwyyxx).
Most of them are the best reproducible results on gym.

### To train on an Atari game:

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

It should run at a speed of 6~10 iteration/s on 1 GPU plus 12+ CPU cores.
Training with a significant slower speed (e.g. on CPU) will give bad performance,
probably because of async issues.
The pre-trained models are all trained with 4 GPUs for about 2 days.

Occasionally processes may not get terminated completely, therefore it is suggested to use systemd-run to run any
multiprocess Python program to get a cgroup dedicated for the task.

### To run a pretrained Atari model for 100 episodes:

1. Download models from [model zoo](https://goo.gl/9yIol2)
2. `ENV=Breakout-v0; ./run-atari.py --load "$ENV".tfmodel --env "$ENV" --episode 100 --output output_dir`

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

+ [AirRaid](https://gym.openai.com/evaluations/eval_zIeNk5MxSGOmvGEUxrZDUw) (this one is flickering due to [gym settings](https://github.com/openai/gym/issues/378))
+ [Alien](https://gym.openai.com/evaluations/eval_8NR1IvjTQkSIT6En4xSMA)
+ [Amidar](https://gym.openai.com/evaluations/eval_HwEazbHtTYGpCialv9uPhA)
+ [Assault](https://gym.openai.com/evaluations/eval_tCiHwy5QrSdFVucSbBV6Q)
+ [Asterix](https://gym.openai.com/evaluations/eval_mees2c58QfKm5GspCjRfCA)
+ [Asteroids](https://gym.openai.com/evaluations/eval_8eHKsRL4RzuZEq9AOLZA)
+ [Atlantis](https://gym.openai.com/evaluations/eval_Z1B3d7A1QCaQk1HpO1Rg)
+ [BankHeist](https://gym.openai.com/evaluations/eval_hifoaxFTIuLlPd38BjnOw)
+ [BattleZone](https://gym.openai.com/evaluations/eval_SoLit2bR1qmFoC0AsJF6Q)
+ [BeamRider](https://gym.openai.com/evaluations/eval_KuOYumrjQjixwL0spG0iCA)
+ [Berzerk](https://gym.openai.com/evaluations/eval_Yri0XQbwRy62NzWILdn5IA)
+ [Breakout](https://gym.openai.com/evaluations/eval_L55gczPrQJamMGihq9tzA)
+ [Carnival](https://gym.openai.com/evaluations/eval_xJSOlo2lSWaH1wHEOX5vw)
+ [Centipede](https://gym.openai.com/evaluations/eval_mc1Kp5e6R42rFdjeMLzkIg)
+ [ChopperCommand](https://gym.openai.com/evaluations/eval_tYVKyh7wQieRIKgEvVaCuw)
+ [CrazyClimber](https://gym.openai.com/evaluations/eval_bKeBg0QwSgOm6A0I0wDhSw)
+ [DemonAttack](https://gym.openai.com/evaluations/eval_tt21vVaRCKYzWFcg1Kw)
+ [DoubleDunk](https://gym.openai.com/evaluations/eval_FI1GpF4TlCuf29KccTpQ)
+ [ElevatorAction](https://gym.openai.com/evaluations/eval_SqeAouMvR0icRivx2xprZg)
+ [FishingDerby](https://gym.openai.com/evaluations/eval_pPLCnFXsTVaayrIboDOs0g)
+ [Frostbite](https://gym.openai.com/evaluations/eval_qtC3taKFSgWwkO9q9IM4hA)
+ [Gopher](https://gym.openai.com/evaluations/eval_KVcpR1YgQkEzrL2VIcAQ)
+ [Gravitar](https://gym.openai.com/evaluations/eval_QudrLdVmTpK9HF5juaZr0w)
+ [IceHockey](https://gym.openai.com/evaluations/eval_8oWCTwwGS7OUTTGRwBPQkQ)
+ [Jamesbond](https://gym.openai.com/evaluations/eval_mLF7XPi8Tw66pnjP73JsmA)
+ [JourneyEscape](https://gym.openai.com/evaluations/eval_S9nQuXLRSu7S5x21Ay6AA)
+ [Kangaroo](https://gym.openai.com/evaluations/eval_TNJiLB8fTqOPfvINnPXoQ)
+ [Krull](https://gym.openai.com/evaluations/eval_dfOS2WzhTh6sn1FuPS9HA)
+ [KungFuMaster](https://gym.openai.com/evaluations/eval_vNWDShYTRC0MhfIybeUYg)
+ [MsPacman](https://gym.openai.com/evaluations/eval_kpL9bSsS4GXsYb9HuEfew)
+ [NameThisGame](https://gym.openai.com/evaluations/eval_LZqfv706SdOMtR4ZZIwIsg)
+ [Phoenix](https://gym.openai.com/evaluations/eval_uzUruiB3RRKUMvJIxvEzYA)
+ [Pong](https://gym.openai.com/evaluations/eval_8L7SV59nSW6GGbbP3N4G6w)
+ [Pooyan](https://gym.openai.com/evaluations/eval_UXFVI34MSAuNTtjZcK8N0A)
+ [Qbert](https://gym.openai.com/evaluations/eval_wekCJkrWQm9NrOUzltXg)
+ [Riverraid](https://gym.openai.com/evaluations/eval_OU4x3DkTfm4uaXy6CIaXg)
+ [RoadRunner](https://gym.openai.com/evaluations/eval_wINKQTwxT9ipydHOXBhg)
+ [Robotank](https://gym.openai.com/evaluations/eval_Gr5c0ld3QACLDPQrGdzbiw)
+ [Seaquest](https://gym.openai.com/evaluations/eval_N2624y3NSJWrOgoMSpOi4w)
+ [SpaceInvaders](https://gym.openai.com/evaluations/eval_Eduozx4HRyqgTCVk9ltw)
+ [StarGunner](https://gym.openai.com/evaluations/eval_JB5cOJXFSS2cTQ7dXK8Iag)
+ [Tennis](https://gym.openai.com/evaluations/eval_gDjJD0MMS1yLm1T0hdqI4g)
+ [Tutankham](https://gym.openai.com/evaluations/eval_gDjJD0MMS1yLm1T0hdqI4g)
+ [UpNDown](https://gym.openai.com/evaluations/eval_KmkvMJkxQFSED20wFUMdIA)
+ [VideoPinball](https://gym.openai.com/evaluations/eval_PWwzNhVFR2CxjYvEsPfT1g)
+ [WizardOfWor](https://gym.openai.com/evaluations/eval_1oGQhphpQhmzEMIYRrrp0A)
+ [Zaxxon](https://gym.openai.com/evaluations/eval_TIQ102EwTrHrOyve2RGfg)

Note that atari game settings in gym are quite different from DeepMind papers, so the scores are not comparable. The most notable differences are:
+ In gym, each action is randomly repeated 2~4 times.
+ In gym, inputs are RGB instead of greyscale.
+ In gym, an episode is limited to 10000 steps.
+ The action space also seems to be different.

Also see the DQN implementation [here](../Atari2600)
