import gym
import numpy as np
from abc import ABC, abstractmethod
from typing import Any, List, Tuple, Optional, Callable
[docs]class EnvWorker(ABC):
"""An abstract worker for an environment."""
def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
self._env_fn = env_fn
self.is_closed = False
self.result: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]
@abstractmethod
def __getattr__(self, key: str) -> Any:
pass
[docs] @abstractmethod
def reset(self) -> Any:
pass
[docs] @abstractmethod
def send_action(self, action: np.ndarray) -> None:
pass
[docs] def get_result(
self,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
return self.result
[docs] def step(
self, action: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Perform one timestep of the environment's dynamic.
"send_action" and "get_result" are coupled in sync simulation, so
typically users only call "step" function. But they can be called
separately in async simulation, i.e. someone calls "send_action" first,
and calls "get_result" later.
"""
self.send_action(action)
return self.get_result()
[docs] @staticmethod
def wait(
workers: List["EnvWorker"],
wait_num: int,
timeout: Optional[float] = None,
) -> List["EnvWorker"]:
"""Given a list of workers, return those ready ones."""
raise NotImplementedError
[docs] @abstractmethod
def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
pass
[docs] @abstractmethod
def render(self, **kwargs: Any) -> Any:
"""Render the environment."""
pass
[docs] @abstractmethod
def close_env(self) -> None:
pass
[docs] def close(self) -> None:
if self.is_closed:
return None
self.is_closed = True
self.close_env()