import gym
import numpy as np
from abc import ABC, abstractmethod
from typing import List, Tuple, Union, Optional, Callable
[docs]class BaseVectorEnv(ABC, gym.Env):
"""Base class for vectorized environments wrapper. Usage:
::
env_num = 8
envs = VectorEnv([lambda: gym.make(task) for _ in range(env_num)])
assert len(envs) == env_num
It accepts a list of environment generators. In other words, an environment
generator ``efn`` of a specific task means that ``efn()`` returns the
environment of the given task, for example, ``gym.make(task)``.
All of the VectorEnv must inherit :class:`~tianshou.env.BaseVectorEnv`.
Here are some other usages:
::
envs.seed(2) # which is equal to the next line
envs.seed([2, 3, 4, 5, 6, 7, 8, 9]) # set specific seed for each env
obs = envs.reset() # reset all environments
obs = envs.reset([0, 5, 7]) # reset 3 specific environments
obs, rew, done, info = envs.step([1] * 8) # step synchronously
envs.render() # render all environments
envs.close() # close all environments
"""
def __init__(self, env_fns: List[Callable[[], gym.Env]]) -> None:
self._env_fns = env_fns
self.env_num = len(env_fns)
[docs] def __len__(self) -> int:
"""Return len(self), which is the number of environments."""
return self.env_num
def __getattribute__(self, key: str):
"""Switch between the default attribute getter or one
looking at wrapped environment level depending on the key."""
if key not in ('observation_space', 'action_space'):
return super().__getattribute__(key)
else:
return self.__getattr__(key)
[docs] @abstractmethod
def __getattr__(self, key: str):
"""Try to retrieve an attribute from each individual wrapped
environment, if it does not belong to the wrapping vector
environment class."""
pass
[docs] @abstractmethod
def reset(self, id: Optional[Union[int, List[int]]] = None):
"""Reset the state of all the environments and return initial
observations if id is ``None``, otherwise reset the specific
environments with given id, either an int or a list.
"""
pass
[docs] @abstractmethod
def step(self,
action: np.ndarray,
id: Optional[Union[int, List[int]]] = None
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Run one timestep of all the environments’ dynamics if id is
``None``, otherwise run one timestep for some environments
with given id, either an int or a list. When the end of
episode is reached, you are responsible for calling reset(id)
to reset this environment’s state.
Accept a batch of action and return a tuple (obs, rew, done, info).
:param numpy.ndarray action: a batch of action provided by the agent.
:return: A tuple including four items:
* ``obs`` a numpy.ndarray, the agent's observation of current \
environments
* ``rew`` a numpy.ndarray, the amount of rewards returned after \
previous actions
* ``done`` a numpy.ndarray, whether these episodes have ended, in \
which case further step() calls will return undefined results
* ``info`` a numpy.ndarray, contains auxiliary diagnostic \
information (helpful for debugging, and sometimes learning)
"""
pass
[docs] @abstractmethod
def seed(self, seed: Optional[Union[int, List[int]]] = None) -> List[int]:
"""Set the seed for all environments.
Accept ``None``, an int (which will extend ``i`` to
``[i, i + 1, i + 2, ...]``) or a list.
:return: The list of seeds used in this env's random number \
generators. The first value in the list should be the "main" seed, or \
the value which a reproducer pass to "seed".
"""
pass
[docs] @abstractmethod
def render(self, **kwargs) -> None:
"""Render all of the environments."""
pass
[docs] @abstractmethod
def close(self) -> None:
"""Close all of the environments.
Environments will automatically close() themselves when garbage
collected or when the program exits.
"""
pass