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lit_sb3.envs.identity_env

source module lit_sb3.envs.identity_env

Classes

source class IdentityEnv[T: (int, np.ndarray)](dim: int | None = None, space: spaces.Space | None = None, ep_length: int = 100)

Bases : gym.Env

Identity environment for testing purposes.

Parameters

  • dim : int | None — the size of the action and observation dimension you want to learn. Provide at most one of dim and space. If both are None, then initialization proceeds with dim=1 and space=None.

  • space : spaces.Space | None — the action and observation space. Provide at most one of dim and space.

  • ep_length : int — the length of each episode in timesteps

Attributes

  • unwrapped : Env[ObsType, ActType] — Returns the base non-wrapped environment.

  • np_random_seed : int — attr:_np_random_seed that if not set will first initialise with a random int as seed.

  • np_random : np.random.Generator — attr:_np_random that if not set will initialise with a random seed.

Methods

source method IdentityEnv.reset(*, seed: int | None = None, options: dict | None = None) → tuple[T, dict]

source method IdentityEnv.step(action: T) → tuple[T, float, bool, bool, dict[str, Any]]

source method IdentityEnv.render(mode: str = 'human') → None

source class IdentityEnvBox(low: float = -1.0, high: float = 1.0, eps: float = 0.05, ep_length: int = 100)

Bases : IdentityEnv[np.ndarray]

Identity environment for testing purposes.

Parameters

  • low : float — the lower bound of the box dim

  • high : float — the upper bound of the box dim

  • eps : float — the epsilon bound for correct value

  • ep_length : int — the length of each episode in timesteps

Attributes

  • unwrapped : Env[ObsType, ActType] — Returns the base non-wrapped environment.

  • np_random_seed : int — attr:_np_random_seed that if not set will first initialise with a random int as seed.

  • np_random : np.random.Generator — attr:_np_random that if not set will initialise with a random seed.

Methods

source method IdentityEnvBox.step(action: np.ndarray) → tuple[np.ndarray, float, bool, bool, dict[str, Any]]

source class IdentityEnvMultiDiscrete(dim: int = 1, ep_length: int = 100)

Bases : IdentityEnv[np.ndarray]

Identity environment for testing purposes.

Parameters

  • dim : int — the size of the dimensions you want to learn

  • ep_length : int — the length of each episode in timesteps

Attributes

  • unwrapped : Env[ObsType, ActType] — Returns the base non-wrapped environment.

  • np_random_seed : int — attr:_np_random_seed that if not set will first initialise with a random int as seed.

  • np_random : np.random.Generator — attr:_np_random that if not set will initialise with a random seed.

source class IdentityEnvMultiBinary(dim: int = 1, ep_length: int = 100)

Bases : IdentityEnv[np.ndarray]

Identity environment for testing purposes.

Parameters

  • dim : int — the size of the dimensions you want to learn

  • ep_length : int — the length of each episode in timesteps

Attributes

  • unwrapped : Env[ObsType, ActType] — Returns the base non-wrapped environment.

  • np_random_seed : int — attr:_np_random_seed that if not set will first initialise with a random int as seed.

  • np_random : np.random.Generator — attr:_np_random that if not set will initialise with a random seed.

source class FakeImageEnv(action_dim: int = 6, screen_height: int = 84, screen_width: int = 84, n_channels: int = 1, discrete: bool = True, channel_first: bool = False)

Bases : gym.Env

Fake image environment for testing purposes, it mimics Atari games.

Parameters

  • action_dim : int — Number of discrete actions

  • screen_height : int — Height of the image

  • screen_width : int — Width of the image

  • n_channels : int — Number of color channels

  • discrete : bool — Create discrete action space instead of continuous

  • channel_first : bool — Put channels on first axis instead of last

Attributes

  • unwrapped : Env[ObsType, ActType] — Returns the base non-wrapped environment.

  • np_random_seed : int — attr:_np_random_seed that if not set will first initialise with a random int as seed.

  • np_random : np.random.Generator — attr:_np_random that if not set will initialise with a random seed.

Methods

source method FakeImageEnv.reset(*, seed: int | None = None, options: dict | None = None) → tuple[np.ndarray, dict]

source method FakeImageEnv.step(action: np.ndarray | int) → GymStepReturn

source method FakeImageEnv.render(mode: str = 'human') → None