38a95c19da
1. DQN learn should keep eps=0 2. Add a warning of env.seed in VecEnv 3. fix #162 of multi-dim action
128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
import gym
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import numpy as np
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from abc import ABC, abstractmethod
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from typing import List, Tuple, Union, Optional, Callable
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class BaseVectorEnv(ABC, gym.Env):
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"""Base class for vectorized environments wrapper. Usage:
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::
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env_num = 8
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envs = VectorEnv([lambda: gym.make(task) for _ in range(env_num)])
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assert len(envs) == env_num
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It accepts a list of environment generators. In other words, an environment
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generator ``efn`` of a specific task means that ``efn()`` returns the
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environment of the given task, for example, ``gym.make(task)``.
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All of the VectorEnv must inherit :class:`~tianshou.env.BaseVectorEnv`.
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Here are some other usages:
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::
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envs.seed(2) # which is equal to the next line
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envs.seed([2, 3, 4, 5, 6, 7, 8, 9]) # set specific seed for each env
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obs = envs.reset() # reset all environments
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obs = envs.reset([0, 5, 7]) # reset 3 specific environments
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obs, rew, done, info = envs.step([1] * 8) # step synchronously
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envs.render() # render all environments
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envs.close() # close all environments
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.. warning::
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If you use your own environment, please make sure the ``seed`` method
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is set up properly, e.g.,
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::
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def seed(self, seed):
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np.random.seed(seed)
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Otherwise, the outputs of these envs may be the same with each other.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]]) -> None:
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self._env_fns = env_fns
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self.env_num = len(env_fns)
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def __len__(self) -> int:
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"""Return len(self), which is the number of environments."""
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return self.env_num
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def __getattribute__(self, key: str):
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"""Switch between the default attribute getter or one
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looking at wrapped environment level depending on the key."""
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if key not in ('observation_space', 'action_space'):
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return super().__getattribute__(key)
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else:
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return self.__getattr__(key)
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@abstractmethod
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def __getattr__(self, key: str):
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"""Try to retrieve an attribute from each individual wrapped
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environment, if it does not belong to the wrapping vector
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environment class."""
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pass
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@abstractmethod
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def reset(self, id: Optional[Union[int, List[int]]] = None):
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"""Reset the state of all the environments and return initial
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observations if id is ``None``, otherwise reset the specific
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environments with given id, either an int or a list.
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"""
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pass
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@abstractmethod
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def step(self,
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action: np.ndarray,
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id: Optional[Union[int, List[int]]] = None
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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"""Run one timestep of all the environments’ dynamics if id is
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``None``, otherwise run one timestep for some environments
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with given id, either an int or a list. When the end of
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episode is reached, you are responsible for calling reset(id)
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to reset this environment’s state.
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Accept a batch of action and return a tuple (obs, rew, done, info).
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:param numpy.ndarray action: a batch of action provided by the agent.
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:return: A tuple including four items:
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* ``obs`` a numpy.ndarray, the agent's observation of current \
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environments
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* ``rew`` a numpy.ndarray, the amount of rewards returned after \
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previous actions
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* ``done`` a numpy.ndarray, whether these episodes have ended, in \
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which case further step() calls will return undefined results
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* ``info`` a numpy.ndarray, contains auxiliary diagnostic \
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information (helpful for debugging, and sometimes learning)
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"""
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pass
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@abstractmethod
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def seed(self, seed: Optional[Union[int, List[int]]] = None) -> List[int]:
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"""Set the seed for all environments.
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Accept ``None``, an int (which will extend ``i`` to
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``[i, i + 1, i + 2, ...]``) or a list.
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:return: The list of seeds used in this env's random number \
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generators. The first value in the list should be the "main" seed, or \
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the value which a reproducer pass to "seed".
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"""
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pass
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@abstractmethod
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def render(self, **kwargs) -> None:
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"""Render all of the environments."""
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pass
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@abstractmethod
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def close(self) -> None:
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"""Close all of the environments.
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Environments will automatically close() themselves when garbage
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collected or when the program exits.
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"""
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pass
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