Atari

We use ale_py==0.7.5 as the codebase. See https://github.com/mgbellemare/Arcade-Learning-Environment/tree/v0.7.5

Env Wrappers

Currently it includes these wrappers: random-noops / fire-reset / episodic-life / frame-skip / action-repeat / image-resize / reward-clip. The wrapper execution order is the same as OpenAI Baselines.

Options

  • task_id (str): see available tasks below;

  • num_envs (int): how many environments you would like to create;

  • batch_size (int): the expected batch size for return result, default to num_envs;

  • num_threads (int): the maximum thread number for executing the actual env.step, default to batch_size;

  • seed (int): the environment seed, default to 42;

  • max_episode_steps (int): the maximum number of steps for one episode, default to 108000;

  • img_height (int): the desired observation image height, default to 84;

  • img_width (int): the desired observation image width, default to 84;

  • stack_num (int): the number of frames to stack for a single observation, default to 4;

  • gray_scale (bool): whether to use gray scale env wrapper, default to True;

  • frame_skip (int): the number of frames to execute one repeated action, only the last frame would be kept, default to 4;

  • noop_max (int): the maximum number of no-op action being executed when calling a single env.reset, default to 30;

  • episodic_life (bool): make end-of-life == end-of-episode, but only reset on true game over. It helps the value estimation. Default to False;

  • zero_discount_on_life_loss (bool): when the agent losses a life, the discount in dm_env.TimeStep is set to 0. This option doesn’t affect gym’s behavior (since there is no discount field in gym’s API). Default to False;

  • reward_clip (bool): whether to change the reward to sign(reward), default to False;

  • repeat_action_probability (float): the action repeat probability in ALE configuration, default to 0 (no action repeat to perform deterministic result);

  • use_inter_area_resize (bool): whether to use cv::INTER_AREA for image resize, default to True.

Observation Space

The observation image size should be (stack_num, img_height, img_width), (4, 84, 84) by default. For a single frame, it has been gray-scaled and resized inside the c++ code.

Action Space

Each Atari games has its own discrete action space.

Available Tasks

Note: Our Atari environments ALE settings follow gym’s *NoFrameSkip-v4 (with openai/baselines wrapper) instead of *-v5 by default, see the related discussions at Issue #14.

  • Adventure-v5

  • AirRaid-v5

  • Alien-v5

  • Amidar-v5

  • Assault-v5

  • Asterix-v5

  • Asteroids-v5

  • Atlantis-v5

  • Atlantis2-v5

  • Backgammon-v5

  • BankHeist-v5

  • BasicMath-v5

  • BattleZone-v5

  • BeamRider-v5

  • Berzerk-v5

  • Blackjack-v5

  • Bowling-v5

  • Boxing-v5

  • Breakout-v5

  • Carnival-v5

  • Casino-v5

  • Centipede-v5

  • ChopperCommand-v5

  • CrazyClimber-v5

  • Crossbow-v5

  • Darkchambers-v5

  • Defender-v5

  • DemonAttack-v5

  • DonkeyKong-v5

  • DoubleDunk-v5

  • Earthworld-v5

  • ElevatorAction-v5

  • Enduro-v5

  • Entombed-v5

  • Et-v5

  • FishingDerby-v5

  • FlagCapture-v5

  • Freeway-v5

  • Frogger-v5

  • Frostbite-v5

  • Galaxian-v5

  • Gopher-v5

  • Gravitar-v5

  • Hangman-v5

  • HauntedHouse-v5

  • Hero-v5

  • HumanCannonball-v5

  • IceHockey-v5

  • Jamesbond-v5

  • JourneyEscape-v5

  • Kaboom-v5

  • Kangaroo-v5

  • KeystoneKapers-v5

  • KingKong-v5

  • Klax-v5

  • Koolaid-v5

  • Krull-v5

  • KungFuMaster-v5

  • LaserGates-v5

  • LostLuggage-v5

  • MarioBros-v5

  • MiniatureGolf-v5

  • MontezumaRevenge-v5

  • MrDo-v5

  • MsPacman-v5

  • NameThisGame-v5

  • Othello-v5

  • Pacman-v5

  • Phoenix-v5

  • Pitfall-v5

  • Pitfall2-v5

  • Pong-v5

  • Pooyan-v5

  • PrivateEye-v5

  • Qbert-v5

  • Riverraid-v5

  • RoadRunner-v5

  • Robotank-v5

  • Seaquest-v5

  • SirLancelot-v5

  • Skiing-v5

  • Solaris-v5

  • SpaceInvaders-v5

  • SpaceWar-v5

  • StarGunner-v5

  • Superman-v5

  • Surround-v5

  • Tennis-v5

  • Tetris-v5

  • TicTacToe3d-v5

  • TimePilot-v5

  • Trondead-v5

  • Turmoil-v5

  • Tutankham-v5

  • UpNDown-v5

  • Venture-v5

  • VideoCheckers-v5

  • VideoChess-v5

  • VideoCube-v5

  • VideoPinball-v5

  • WizardOfWor-v5

  • WordZapper-v5

  • YarsRevenge-v5

  • Zaxxon-v5