lit_sb3.utils.spaces

source module lit_sb3.utils.spaces

Gymnasium observation-space shape validation helpers.

Functions

• check_for_correct_spaces — Checks that the environment has same spaces as provided ones. Used by BaseAlgorithm to check if.

• check_shape_equal — If the spaces are Box, check that they have the same shape.

• is_vectorized_box_observation — For box observation type, detects and validates the shape,.

• is_vectorized_discrete_observation — For discrete observation type, detects and validates the shape,.

• is_vectorized_multidiscrete_observation — For multidiscrete observation type, detects and validates the shape,.

• is_vectorized_multibinary_observation — For multibinary observation type, detects and validates the shape,.

• is_vectorized_dict_observation — For dict observation type, detects and validates the shape,.

• is_vectorized_observation — For every observation type, detects and validates the shape,.

source check_for_correct_spaces(env: GymEnv, observation_space: spaces.Space, action_space: spaces.Space) → None

Checks that the environment has same spaces as provided ones. Used by BaseAlgorithm to check if.

spaces match after loading the model with given env. Checked parameters: - observation_space - action_space.

Parameters

• env : GymEnv — Environment to check for valid spaces

• observation_space : spaces.Space — Observation space to check against

• action_space : spaces.Space — Action space to check against

Raises

• ValueError

source check_shape_equal(space1: spaces.Space, space2: spaces.Space) → None

If the spaces are Box, check that they have the same shape.

If the spaces are Dict, it recursively checks the subspaces.

Parameters

• space1 : spaces.Space — Space

• space2 : spaces.Space — Other space

source is_vectorized_box_observation(observation: np.ndarray, observation_space: spaces.Box) → bool

For box observation type, detects and validates the shape,.

then returns whether or not the observation is vectorized.

Parameters

• observation : np.ndarray — the input observation to validate

• observation_space : spaces.Box — the observation space

Returns

• bool — whether the given observation is vectorized or not

Raises

• ValueError

source is_vectorized_discrete_observation(observation: int | np.ndarray, observation_space: spaces.Discrete) → bool

For discrete observation type, detects and validates the shape,.

then returns whether or not the observation is vectorized.

Parameters

• observation : int | np.ndarray — the input observation to validate

• observation_space : spaces.Discrete — the observation space

Returns

• bool — whether the given observation is vectorized or not

Raises

• ValueError

source is_vectorized_multidiscrete_observation(observation: np.ndarray, observation_space: spaces.MultiDiscrete) → bool

For multidiscrete observation type, detects and validates the shape,.

then returns whether or not the observation is vectorized.

Parameters

• observation : np.ndarray — the input observation to validate

• observation_space : spaces.MultiDiscrete — the observation space

Returns

• bool — whether the given observation is vectorized or not

Raises

• ValueError

source is_vectorized_multibinary_observation(observation: np.ndarray, observation_space: spaces.MultiBinary) → bool

For multibinary observation type, detects and validates the shape,.

then returns whether or not the observation is vectorized.

Parameters

• observation : np.ndarray — the input observation to validate

• observation_space : spaces.MultiBinary — the observation space

Returns

• bool — whether the given observation is vectorized or not

Raises

• ValueError

source is_vectorized_dict_observation(observation: np.ndarray, observation_space: spaces.Dict) → bool

For dict observation type, detects and validates the shape,.

then returns whether or not the observation is vectorized.

Parameters

• observation : np.ndarray — the input observation to validate

• observation_space : spaces.Dict — the observation space

Returns

• bool — whether the given observation is vectorized or not

Raises

• ValueError

source is_vectorized_observation(observation: int | np.ndarray, observation_space: spaces.Space) → bool

For every observation type, detects and validates the shape,.

then returns whether or not the observation is vectorized.

Parameters

• observation : int | np.ndarray — the input observation to validate

• observation_space : spaces.Space — the observation space

Returns

• bool — whether the given observation is vectorized or not

Raises

• ValueError