43792bf5ab
fixes some deprecation warnings due to new changes in gym version 0.23: - use `env.np_random.integers` instead of `env.np_random.randint` - support `seed` and `return_info` arguments for reset (addresses https://github.com/thu-ml/tianshou/issues/605)
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
from typing import Any, Callable, List, Optional, Tuple, Union
|
|
|
|
import gym
|
|
import numpy as np
|
|
|
|
from tianshou.env.worker import EnvWorker
|
|
|
|
try:
|
|
import ray
|
|
except ImportError:
|
|
pass
|
|
|
|
|
|
class _SetAttrWrapper(gym.Wrapper):
|
|
|
|
def set_env_attr(self, key: str, value: Any) -> None:
|
|
setattr(self.env, key, value)
|
|
|
|
def get_env_attr(self, key: str) -> Any:
|
|
return getattr(self.env, key)
|
|
|
|
|
|
class RayEnvWorker(EnvWorker):
|
|
"""Ray worker used in RayVectorEnv."""
|
|
|
|
def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
|
|
self.env = ray.remote(_SetAttrWrapper).options( # type: ignore
|
|
num_cpus=0
|
|
).remote(env_fn())
|
|
super().__init__(env_fn)
|
|
|
|
def get_env_attr(self, key: str) -> Any:
|
|
return ray.get(self.env.get_env_attr.remote(key))
|
|
|
|
def set_env_attr(self, key: str, value: Any) -> None:
|
|
ray.get(self.env.set_env_attr.remote(key, value))
|
|
|
|
def reset(self, **kwargs: Any) -> Any:
|
|
if "seed" in kwargs:
|
|
super().seed(kwargs["seed"])
|
|
return ray.get(self.env.reset.remote(**kwargs))
|
|
|
|
@staticmethod
|
|
def wait( # type: ignore
|
|
workers: List["RayEnvWorker"], wait_num: int, timeout: Optional[float] = None
|
|
) -> List["RayEnvWorker"]:
|
|
results = [x.result for x in workers]
|
|
ready_results, _ = ray.wait(results, num_returns=wait_num, timeout=timeout)
|
|
return [workers[results.index(result)] for result in ready_results]
|
|
|
|
def send(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
|
|
# self.result is actually a handle
|
|
if action is None:
|
|
self.result = self.env.reset.remote(**kwargs)
|
|
else:
|
|
self.result = self.env.step.remote(action)
|
|
|
|
def recv(
|
|
self
|
|
) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], np.ndarray]:
|
|
return ray.get(self.result) # type: ignore
|
|
|
|
def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
|
|
super().seed(seed)
|
|
try:
|
|
return ray.get(self.env.seed.remote(seed))
|
|
except NotImplementedError:
|
|
self.env.reset.remote(seed=seed)
|
|
return None
|
|
|
|
def render(self, **kwargs: Any) -> Any:
|
|
return ray.get(self.env.render.remote(**kwargs))
|
|
|
|
def close_env(self) -> None:
|
|
ray.get(self.env.close.remote())
|