09692c84fe
Change the behavior of to_numpy and to_torch: from now on, dict is automatically converted to Batch and list is automatically converted to np.ndarray (if an error occurs, raise the exception instead of converting each element in the list).
365 lines
15 KiB
Python
365 lines
15 KiB
Python
import gym
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import numpy as np
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from typing import Any, List, Union, Optional, Callable
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from tianshou.utils import RunningMeanStd
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from tianshou.env.worker import EnvWorker, DummyEnvWorker, SubprocEnvWorker, \
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RayEnvWorker
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class BaseVectorEnv(gym.Env):
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"""Base class for vectorized environments wrapper.
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Usage:
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::
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env_num = 8
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envs = DummyVectorEnv([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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:param env_fns: a list of callable envs, ``env_fns[i]()`` generates the ith env.
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:param worker_fn: a callable worker, ``worker_fn(env_fns[i])`` generates a
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worker which contains the i-th env.
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:param int wait_num: use in asynchronous simulation if the time cost of
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``env.step`` varies with time and synchronously waiting for all
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environments to finish a step is time-wasting. In that case, we can
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return when ``wait_num`` environments finish a step and keep on
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simulation in these environments. If ``None``, asynchronous simulation
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is disabled; else, ``1 <= wait_num <= env_num``.
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:param float timeout: use in asynchronous simulation same as above, in each
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vectorized step it only deal with those environments spending time
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within ``timeout`` seconds.
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:param bool norm_obs: Whether to track mean/std of data and normalise observation
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on return. For now, observation normalization only support observation of
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type np.ndarray.
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:param obs_rms: class to track mean&std of observation. If not given, it will
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initialize a new one. Usually in envs that is used to evaluate algorithm,
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obs_rms should be passed in. Default to None.
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:param bool update_obs_rms: Whether to update obs_rms. Default to True.
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"""
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def __init__(
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self,
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env_fns: List[Callable[[], gym.Env]],
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worker_fn: Callable[[Callable[[], gym.Env]], EnvWorker],
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wait_num: Optional[int] = None,
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timeout: Optional[float] = None,
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norm_obs: bool = False,
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obs_rms: Optional[RunningMeanStd] = None,
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update_obs_rms: bool = True,
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) -> None:
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self._env_fns = env_fns
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# A VectorEnv contains a pool of EnvWorkers, which corresponds to
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# interact with the given envs (one worker <-> one env).
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self.workers = [worker_fn(fn) for fn in env_fns]
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self.worker_class = type(self.workers[0])
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assert issubclass(self.worker_class, EnvWorker)
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assert all([isinstance(w, self.worker_class) for w in self.workers])
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self.env_num = len(env_fns)
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self.wait_num = wait_num or len(env_fns)
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assert 1 <= self.wait_num <= len(env_fns), \
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f"wait_num should be in [1, {len(env_fns)}], but got {wait_num}"
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self.timeout = timeout
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assert self.timeout is None or self.timeout > 0, \
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f"timeout is {timeout}, it should be positive if provided!"
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self.is_async = self.wait_num != len(env_fns) or timeout is not None
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self.waiting_conn: List[EnvWorker] = []
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# environments in self.ready_id is actually ready
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# but environments in self.waiting_id are just waiting when checked,
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# and they may be ready now, but this is not known until we check it
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# in the step() function
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self.waiting_id: List[int] = []
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# all environments are ready in the beginning
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self.ready_id = list(range(self.env_num))
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self.is_closed = False
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# initialize observation running mean/std
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self.norm_obs = norm_obs
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self.update_obs_rms = update_obs_rms
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self.obs_rms = RunningMeanStd() if obs_rms is None and norm_obs else obs_rms
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self.__eps = np.finfo(np.float32).eps.item()
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def _assert_is_not_closed(self) -> None:
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assert not self.is_closed, \
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f"Methods of {self.__class__.__name__} cannot be called after close."
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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) -> Any:
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"""Switch the attribute getter depending on the key.
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Any class who inherits ``gym.Env`` will inherit some attributes, like
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``action_space``. However, we would like the attribute lookup to go straight
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into the worker (in fact, this vector env's action_space is always None).
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"""
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if key in ['metadata', 'reward_range', 'spec', 'action_space',
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'observation_space']: # reserved keys in gym.Env
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return self.__getattr__(key)
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else:
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return super().__getattribute__(key)
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def __getattr__(self, key: str) -> List[Any]:
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"""Fetch a list of env attributes.
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This function tries to retrieve an attribute from each individual wrapped
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environment, if it does not belong to the wrapping vector environment class.
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"""
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return [getattr(worker, key) for worker in self.workers]
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def _wrap_id(
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self, id: Optional[Union[int, List[int], np.ndarray]] = None
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) -> Union[List[int], np.ndarray]:
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if id is None:
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return list(range(self.env_num))
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return [id] if np.isscalar(id) else id # type: ignore
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def _assert_id(self, id: Union[List[int], np.ndarray]) -> None:
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for i in id:
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assert i not in self.waiting_id, \
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f"Cannot interact with environment {i} which is stepping now."
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assert i in self.ready_id, \
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f"Can only interact with ready environments {self.ready_id}."
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def reset(
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self, id: Optional[Union[int, List[int], np.ndarray]] = None
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) -> np.ndarray:
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"""Reset the state of some envs and return initial observations.
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If id is None, reset the state of all the environments and return
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initial observations, otherwise reset the specific environments with
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the given id, either an int or a list.
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"""
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self._assert_is_not_closed()
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id = self._wrap_id(id)
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if self.is_async:
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self._assert_id(id)
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obs = np.stack([self.workers[i].reset() for i in id])
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if self.obs_rms and self.update_obs_rms:
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self.obs_rms.update(obs)
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return self.normalize_obs(obs)
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def step(
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self,
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action: np.ndarray,
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id: Optional[Union[int, List[int], np.ndarray]] = None
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) -> List[np.ndarray]:
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"""Run one timestep of some environments' dynamics.
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If id is None, run one timestep of all the environments’ dynamics;
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otherwise run one timestep for some environments with given id, either
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an int or a list. When the end of episode is reached, you are
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responsible for calling reset(id) to reset this environment’s state.
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Accept a batch of action and return a tuple (batch_obs, batch_rew,
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batch_done, batch_info) in numpy format.
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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 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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For the async simulation:
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Provide the given action to the environments. The action sequence
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should correspond to the ``id`` argument, and the ``id`` argument
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should be a subset of the ``env_id`` in the last returned ``info``
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(initially they are env_ids of all the environments). If action is
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None, fetch unfinished step() calls instead.
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"""
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self._assert_is_not_closed()
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id = self._wrap_id(id)
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if not self.is_async:
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assert len(action) == len(id)
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for i, j in enumerate(id):
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self.workers[j].send_action(action[i])
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result = []
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for j in id:
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obs, rew, done, info = self.workers[j].get_result()
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info["env_id"] = j
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result.append((obs, rew, done, info))
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else:
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if action is not None:
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self._assert_id(id)
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assert len(action) == len(id)
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for i, (act, env_id) in enumerate(zip(action, id)):
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self.workers[env_id].send_action(act)
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self.waiting_conn.append(self.workers[env_id])
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self.waiting_id.append(env_id)
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self.ready_id = [x for x in self.ready_id if x not in id]
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ready_conns: List[EnvWorker] = []
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while not ready_conns:
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ready_conns = self.worker_class.wait(
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self.waiting_conn, self.wait_num, self.timeout)
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result = []
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for conn in ready_conns:
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waiting_index = self.waiting_conn.index(conn)
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self.waiting_conn.pop(waiting_index)
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env_id = self.waiting_id.pop(waiting_index)
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obs, rew, done, info = conn.get_result()
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info["env_id"] = env_id
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result.append((obs, rew, done, info))
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self.ready_id.append(env_id)
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obs_stack, rew_stack, done_stack, info_stack = map(np.stack, zip(*result))
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if self.obs_rms and self.update_obs_rms:
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self.obs_rms.update(obs_stack)
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return [self.normalize_obs(obs_stack), rew_stack, done_stack, info_stack]
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def seed(
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self, seed: Optional[Union[int, List[int]]] = None
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) -> List[Optional[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 generators.
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The first value in the list should be the "main" seed, or the value
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which a reproducer pass to "seed".
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"""
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self._assert_is_not_closed()
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seed_list: Union[List[None], List[int]]
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if seed is None:
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seed_list = [seed] * self.env_num
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elif isinstance(seed, int):
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seed_list = [seed + i for i in range(self.env_num)]
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else:
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seed_list = seed
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return [w.seed(s) for w, s in zip(self.workers, seed_list)]
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def render(self, **kwargs: Any) -> List[Any]:
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"""Render all of the environments."""
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self._assert_is_not_closed()
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if self.is_async and len(self.waiting_id) > 0:
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raise RuntimeError(
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f"Environments {self.waiting_id} are still stepping, cannot "
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"render them now.")
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return [w.render(**kwargs) for w in self.workers]
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def close(self) -> None:
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"""Close all of the environments.
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This function will be called only once (if not, it will be called during
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garbage collected). This way, ``close`` of all workers can be assured.
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"""
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self._assert_is_not_closed()
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for w in self.workers:
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w.close()
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self.is_closed = True
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def normalize_obs(self, obs: np.ndarray) -> np.ndarray:
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"""Normalize observations by statistics in obs_rms."""
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if self.obs_rms and self.norm_obs:
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clip_max = 10.0 # this magic number is from openai baselines
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# see baselines/common/vec_env/vec_normalize.py#L10
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obs = (obs - self.obs_rms.mean) / np.sqrt(self.obs_rms.var + self.__eps)
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obs = np.clip(obs, -clip_max, clip_max) # type: ignore
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return obs
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def __del__(self) -> None:
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"""Redirect to self.close()."""
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if not self.is_closed:
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self.close()
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class DummyVectorEnv(BaseVectorEnv):
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"""Dummy vectorized environment wrapper, implemented in for-loop.
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.. seealso::
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Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
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super().__init__(env_fns, DummyEnvWorker, **kwargs)
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class SubprocVectorEnv(BaseVectorEnv):
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"""Vectorized environment wrapper based on subprocess.
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.. seealso::
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Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
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def worker_fn(fn: Callable[[], gym.Env]) -> SubprocEnvWorker:
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return SubprocEnvWorker(fn, share_memory=False)
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super().__init__(env_fns, worker_fn, **kwargs)
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class ShmemVectorEnv(BaseVectorEnv):
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"""Optimized SubprocVectorEnv with shared buffers to exchange observations.
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ShmemVectorEnv has exactly the same API as SubprocVectorEnv.
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.. seealso::
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Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
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def worker_fn(fn: Callable[[], gym.Env]) -> SubprocEnvWorker:
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return SubprocEnvWorker(fn, share_memory=True)
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super().__init__(env_fns, worker_fn, **kwargs)
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class RayVectorEnv(BaseVectorEnv):
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"""Vectorized environment wrapper based on ray.
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This is a choice to run distributed environments in a cluster.
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.. seealso::
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Please refer to :class:`~tianshou.env.BaseVectorEnv` for other APIs' usage.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]], **kwargs: Any) -> None:
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try:
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import ray
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except ImportError as e:
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raise ImportError(
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"Please install ray to support RayVectorEnv: pip install ray"
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) from e
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if not ray.is_initialized():
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ray.init()
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super().__init__(env_fns, RayEnvWorker, **kwargs)
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