94bfb32cc1
1. add policy.eval() in all test scripts' "watch performance" 2. remove dict return support for collector preprocess_fn 3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)` 4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184) 5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard 6. add test_returns (both GAE and nstep) 7. change the type-checking order in batch.py and converter.py in order to meet the most often case first 8. fix shape inconsistency for torch.Tensor in replay buffer 9. remove `**kwargs` in ReplayBuffer 10. remove default value in batch.split() and add merge_last argument (#185) 11. improve nstep efficiency 12. add max_batchsize in onpolicy algorithms 13. potential bugfix for subproc.wait 14. fix RecurrentActorProb 15. improve the code-coverage (from 90% to 95%) and remove the dead code 16. fix some incorrect type annotation The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
342 lines
14 KiB
Python
342 lines
14 KiB
Python
import gym
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import warnings
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import numpy as np
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from typing import List, Union, Optional, Callable, Any
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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. 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
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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 this 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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"""
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def __init__(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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) -> 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 = []
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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 = []
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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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def _assert_is_not_closed(self) -> None:
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assert not self.is_closed, f"Methods of {self.__class__.__name__} "\
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"should not 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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"""Any class who inherits ``gym.Env`` will inherit some attributes,
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like ``action_space``. However, we would like the attribute lookup to
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go straight into the worker (in fact, this vector env's action_space
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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) -> Any:
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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 environment
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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(self, id: Optional[Union[int, List[int], np.ndarray]] = None
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) -> List[int]:
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if id is None:
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id = list(range(self.env_num))
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elif np.isscalar(id):
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id = [id]
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return id
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def _assert_id(self, id: List[int]) -> 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(self, id: Optional[Union[int, List[int], np.ndarray]] = None
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) -> np.ndarray:
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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 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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return obs
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def step(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 all the environments’ dynamics if id is "None",
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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 \
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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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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, result = [], []
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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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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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return list(map(np.stack, zip(*result)))
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def seed(self,
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seed: Optional[Union[int, List[int]]] = None) -> List[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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if seed is None:
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seed = [seed] * self.env_num
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elif np.isscalar(seed):
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seed = [seed + i for i in range(self.env_num)]
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return [w.seed(s) for w, s in zip(self.workers, seed)]
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def render(self, **kwargs) -> 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 "
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f"stepping, cannot 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. This function will be called only
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once (if not, it will be called during garbage collected). This way,
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``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 __del__(self) -> None:
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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 more detailed
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explanation.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]],
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wait_num: Optional[int] = None,
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timeout: Optional[float] = None) -> None:
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super().__init__(env_fns, DummyEnvWorker,
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wait_num=wait_num, timeout=timeout)
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class VectorEnv(DummyVectorEnv):
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def __init__(self, *args, **kwargs) -> None:
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warnings.warn(
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'VectorEnv is renamed to DummyVectorEnv, and will be removed in '
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'0.3. Use DummyVectorEnv instead!', Warning)
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super().__init__(*args, **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 more detailed
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explanation.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]],
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wait_num: Optional[int] = None,
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timeout: Optional[float] = None) -> None:
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def worker_fn(fn):
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return SubprocEnvWorker(fn, share_memory=False)
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super().__init__(env_fns, worker_fn,
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wait_num=wait_num, timeout=timeout)
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class ShmemVectorEnv(BaseVectorEnv):
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"""Optimized version of SubprocVectorEnv which uses shared variables to
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communicate observations. ShmemVectorEnv has exactly the same API as
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SubprocVectorEnv.
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.. seealso::
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Please refer to :class:`~tianshou.env.SubprocVectorEnv` for more
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detailed explanation.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]],
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wait_num: Optional[int] = None,
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timeout: Optional[float] = None) -> None:
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def worker_fn(fn):
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return SubprocEnvWorker(fn, share_memory=True)
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super().__init__(env_fns, worker_fn,
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wait_num=wait_num, timeout=timeout)
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class RayVectorEnv(BaseVectorEnv):
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"""Vectorized environment wrapper based on
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`ray <https://github.com/ray-project/ray>`_. This is a choice to run
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distributed environments in a cluster.
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.. seealso::
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Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
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explanation.
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"""
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def __init__(self, env_fns: List[Callable[[], gym.Env]],
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wait_num: Optional[int] = None,
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timeout: Optional[float] = None) -> 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,
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wait_num=wait_num, timeout=timeout)
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