import gym
import numpy as np
from typing import List, Callable, Tuple, Optional, Any
from tianshou.env.worker import EnvWorker
try:
import ray
except ImportError:
pass
[docs]class RayEnvWorker(EnvWorker):
"""Ray worker used in RayVectorEnv."""
def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
super().__init__(env_fn)
self.env = ray.remote(gym.Wrapper).options(num_cpus=0).remote(env_fn())
[docs] def __getattr__(self, key: str) -> Any:
return ray.get(self.env.__getattr__.remote(key))
[docs] def reset(self) -> Any:
return ray.get(self.env.reset.remote())
[docs] @staticmethod
def wait(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]
[docs] def send_action(self, action: np.ndarray) -> None:
# self.action is actually a handle
self.result = self.env.step.remote(action)
[docs] def get_result(self) -> Tuple[
np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
return ray.get(self.result)
[docs] def seed(self, seed: Optional[int] = None) -> List[int]:
if hasattr(self.env, 'seed'):
return ray.get(self.env.seed.remote(seed))
return None
[docs] def render(self, **kwargs) -> Any:
if hasattr(self.env, 'render'):
return ray.get(self.env.render.remote(**kwargs))
return None
[docs] def close_env(self) -> None:
ray.get(self.env.close.remote())