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.
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
from typing import Any, Callable, List, Optional, Union
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import gym
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import numpy as np
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from tianshou.env.utils import gym_new_venv_step_type, gym_old_venv_step_type
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from tianshou.env.worker import EnvWorker
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try:
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import ray
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except ImportError:
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pass
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class _SetAttrWrapper(gym.Wrapper):
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def __init__(self, env: gym.Env) -> None:
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"""Constructor of this wrapper.
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For Gym 0.25, wrappers will automatically
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change to the old step API. We need to check
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which API ``env`` follows and adjust the
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wrapper accordingly.
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"""
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env.reset()
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step_result = env.step(env.action_space.sample())
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new_step_api = len(step_result) == 5
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try:
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super().__init__(env, new_step_api=new_step_api) # type: ignore
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except TypeError: # The kwarg `new_step_api` was removed in Gym 0.26
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super().__init__(env)
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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 get_env_attr(self, key: str) -> Any:
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return getattr(self.env, key)
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class RayEnvWorker(EnvWorker):
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"""Ray worker used in RayVectorEnv."""
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def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
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self.env = ray.remote(_SetAttrWrapper).options( # type: ignore
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num_cpus=0
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).remote(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 ray.get(self.env.get_env_attr.remote(key))
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def set_env_attr(self, key: str, value: Any) -> None:
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ray.get(self.env.set_env_attr.remote(key, value))
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def reset(self, **kwargs: Any) -> Any:
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if "seed" in kwargs:
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super().seed(kwargs["seed"])
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return ray.get(self.env.reset.remote(**kwargs))
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@staticmethod
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def wait( # type: ignore
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workers: List["RayEnvWorker"], wait_num: int, timeout: Optional[float] = None
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) -> List["RayEnvWorker"]:
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results = [x.result for x in workers]
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ready_results, _ = ray.wait(results, num_returns=wait_num, timeout=timeout)
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return [workers[results.index(result)] for result in ready_results]
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def send(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
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# self.result is actually a handle
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if action is None:
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self.result = self.env.reset.remote(**kwargs)
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else:
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self.result = self.env.step.remote(action)
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def recv(self) -> Union[gym_old_venv_step_type, gym_new_venv_step_type]:
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return ray.get(self.result) # 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 ray.get(self.env.seed.remote(seed))
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except (AttributeError, NotImplementedError):
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self.env.reset.remote(seed=seed)
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return None
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def render(self, **kwargs: Any) -> Any:
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return ray.get(self.env.render.remote(**kwargs))
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def close_env(self) -> None:
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ray.get(self.env.close.remote())
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