ea36dc5195
* Changes to support Gym 0.26.0 * Replace map by simpler list comprehension * Use syntax that is compatible with python 3.7 * Format code * Fix environment seeding in test environment, fix buffer_profile test * Remove self.seed() from __init__ * Fix random number generation * Fix throughput tests * Fix tests * Removed done field from Buffer, fixed throughput test, turned off wandb, fixed formatting, fixed type hints, allow preprocessing_fn with truncated and terminated arguments, updated docstrings * fix lint * fix * fix import * fix * fix mypy * pytest --ignore='test/3rd_party' * Use correct step API in _SetAttrWrapper * Format * Fix mypy * Format * Fix pydocstyle.
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
from typing import Any, Callable, List, Optional, Tuple, Union
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import gym
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import numpy as np
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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 get_env_attr(self, key: str) -> Any:
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return getattr(self.env, key)
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def set_env_attr(self, key: str, value: Any) -> None:
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setattr(self.env.unwrapped, key, value)
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def reset(self, **kwargs: Any) -> Union[np.ndarray, Tuple[np.ndarray, dict]]:
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if "seed" in kwargs:
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super().seed(kwargs["seed"])
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return self.env.reset(**kwargs)
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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(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
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if action is None:
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self.result = self.env.reset(**kwargs)
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else:
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self.result = self.env.step(action) # type: ignore
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def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
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super().seed(seed)
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try:
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return self.env.seed(seed) # type: ignore
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except (AttributeError, NotImplementedError):
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self.env.reset(seed=seed)
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return [seed] # type: ignore
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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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