09692c84fe
Change the behavior of to_numpy and to_torch: from now on, dict is automatically converted to Batch and list is automatically converted to np.ndarray (if an error occurs, raise the exception instead of converting each element in the list).
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
import gym
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import numpy as np
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from typing import Any, List, Callable, Optional
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from tianshou.env.worker import EnvWorker
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class DummyEnvWorker(EnvWorker):
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"""Dummy worker used in sequential vector environments."""
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def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
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self.env = env_fn()
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super().__init__(env_fn)
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def __getattr__(self, key: str) -> Any:
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return getattr(self.env, key)
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def reset(self) -> Any:
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return self.env.reset()
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@staticmethod
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def wait( # type: ignore
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workers: List["DummyEnvWorker"], wait_num: int, timeout: Optional[float] = None
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) -> List["DummyEnvWorker"]:
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# Sequential EnvWorker objects are always ready
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return workers
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def send_action(self, action: np.ndarray) -> None:
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self.result = self.env.step(action)
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def seed(self, seed: Optional[int] = None) -> List[int]:
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super().seed(seed)
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return self.env.seed(seed)
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def render(self, **kwargs: Any) -> Any:
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return self.env.render(**kwargs)
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def close_env(self) -> None:
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self.env.close()
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