base

Source code: tianshou/env/worker/base.py

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class EnvWorker(env_fn: Callable[[], Env])

An abstract worker for an environment.

close() → None

abstract close_env() → None

abstract get_env_attr(key: str) → Any

recv() → tuple[ndarray, ndarray, ndarray, ndarray, ndarray] | tuple[ndarray, dict]

Receive result from low-level worker.

If the last “send” function sends a NULL action, it only returns a single observation; otherwise it returns a tuple of (obs, rew, done, info) or (obs, rew, terminated, truncated, info), based on whether the environment is using the old step API or the new one.

abstract render(**kwargs: Any) → Any

Render the environment.

abstract reset(**kwargs: Any) → tuple[ndarray, dict]

seed(seed: int | None = None) → list[int] | None

send(action: ndarray | None) → None

Send action signal to low-level worker.

When action is None, it indicates sending “reset” signal; otherwise it indicates “step” signal. The paired return value from “recv” function is determined by such kind of different signal.

abstract set_env_attr(key: str, value: Any) → None

step(action: ndarray) → tuple[ndarray, ndarray, ndarray, ndarray, ndarray]

Perform one timestep of the environment’s dynamic.

“send” and “recv” are coupled in sync simulation, so users only call “step” function. But they can be called separately in async simulation, i.e. someone calls “send” first, and calls “recv” later.

static wait(workers: list[EnvWorker], wait_num: int, timeout: float | None = None) → list[EnvWorker]

Given a list of workers, return those ready ones.