Petting Zoo Vector Base Class

Base class for vectorized pettingzoo environments.

Parameters

class agilerl.vector.pz_vec_env.PettingZooVecEnv(num_envs: int, observation_spaces: dict[str, Space], action_spaces: dict[str, Space], possible_agents: list[str], *, metadata: dict[str, Any] | None = None, render_mode: str | None = None)

An abstract asynchronous, vectorized environment.

References:

https://github.com/openai/baselines/tree/master/baselines/common/vec_env https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/vector/vector_env.py

Parameters:

  • num_envs (int) – Number of environments to vectorize

  • observation_spaces (dict[str, gymnasium.spaces.Space]) – Dictionary of observation spaces

  • action_spaces (dict[str, gymnasium.spaces.Space]) – Dictionary of action spaces keyed by agent

  • possible_agents (list[str]) – List of possible agents

property action_space

Return callable to get an agent’s action space.

close(**kwargs: Any) → None

Clean up the environments’ resources.

close_extras(*, timeout: float | None = None, terminate: bool = False, **kwargs: Any) → None

Clean up the extra resources e.g. beyond what’s in this base class.

Subclasses may override with compatible signatures (same params or wider).

property observation_space

Return callable to get an agent’s observation space.

render() → Any

Return the rendered frames from the parallel environments.

reset(*, seed: int | None = None, options: dict[str, Any] | None = None) → tuple[dict[str, ndarray], dict[str, Any]]

Reset all the environments and return two dictionaries of batched observations and infos.

Parameters:

  • seed (None | int, optional) – Random seed, defaults to None

  • options (dict[str, Any]) – Options dictionary

Returns:

Tuple of (observations, infos)

Return type:

tuple[dict[str, np.ndarray], dict[str, Any]]

property single_action_space

Return callable to get an agent’s single action space.

property single_observation_space

Return callable to get an agent’s single observation space.

step(actions: dict[str, ndarray], *args: Any, **kwargs: Any) → tuple[dict[str, ndarray | dict[str, ndarray] | tuple[ndarray, ...]], dict[str, ndarray], dict[str, ndarray], dict[str, ndarray], dict[str, Any]]

Take an action for each parallel environment.

Parameters:

actions (dict[str, np.ndarray]) – Dictionary of vectorized actions for each agent.

Returns:

Tuple of observations, rewards, terminated, truncated, infos

Return type:

tuple[dict[str, np.ndarray], dict[str, float], dict[str, bool], dict[str, bool], dict[str, Any]]

step_async(actions: list[dict[str, int | float | ndarray | Tensor]]) → None

Tell all the environments to start taking a step with the given actions. Call step_wait() to get the results of the step. You should not call this if a step_async run is already pending.

Parameters:

actions – List of dictionaries of length num_envs, each sub dictionary contains

actions for each agent in a given environment :type actions: list[dict[str, int | float | np.ndarray]]

step_wait(timeout: float | None = None) → tuple[dict[str, ndarray | dict[str, ndarray] | tuple[ndarray, ...]], dict[str, ndarray], dict[str, ndarray], dict[str, ndarray], dict[str, Any]]

Wait for the step taken with step_async().

Parameters:

timeout – Number of seconds before the call times out. If None, never times out.

property unwrapped: PettingZooVecEnv

Return the base environment.