mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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49d8299a1e
* WIP refactor info API sync vector. * Add missing untracked file. * Add info strategy to reset_wait. * Add interface and docstring. * info with strategy pattern on async vector env. * Add default to async vecenv. * episode statistics for asyncvecnev. * Add tests info strategy format. * Add info strategy to reset_wait. * refactor and cleanup. * Code cleanup. Add tests. * Add tests for video recording with new info format. * fix test case. * fix camelcase. * rename enum. * update tests, docstrings, cleanup. * Changes brax strategy to numpy. add_strategy method in StrategyFactory. Add tests. * fix docstring and logging format. * Set Brax info format as default. Remove classic info format. Update tests. * breaking the wrong loop. * WIP: wrapper. * Add wrapper for brax to classic info. * WIP: wrapper with nested RecordEpisodeStatistic. * Add tests. Refactor docstrings. Cleanup. * cleanup. * patch conflicts. * rebase and conflicts. * new pre-commit conventions. * docstring. * renaming. * incorporate info_processor in vecEnv. * renaming. Create info dict only if needed. * remove all brax references. update docstring. Update duplicate test. * reviews. * pre-commit. * reviews. * docstring. * cleanup blank lines. * add support for numpy dtypes. * docstring fix. * formatting. * naming. * assert correct info from wrappers chaining. Test correct wrappers chaining. naming. * simplify episode_statistics. * change args orer. * update tests. * wip: refactor episode_statistics. * Add test for add_vecore_episode_statistics.
232 lines
8.4 KiB
Python
232 lines
8.4 KiB
Python
"""A synchronous vector environment."""
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from __future__ import annotations
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from copy import deepcopy
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from typing import Any, Iterator, Optional, Sequence, Union
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import numpy as np
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from gym.spaces import Space
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from gym.vector.utils import concatenate, create_empty_array, iterate
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from gym.vector.vector_env import VectorEnv
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__all__ = ["SyncVectorEnv"]
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class SyncVectorEnv(VectorEnv):
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"""Vectorized environment that serially runs multiple environments.
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Example::
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>>> import gym
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>>> env = gym.vector.SyncVectorEnv([
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... lambda: gym.make("Pendulum-v0", g=9.81),
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... lambda: gym.make("Pendulum-v0", g=1.62)
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... ])
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>>> env.reset()
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array([[-0.8286432 , 0.5597771 , 0.90249056],
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[-0.85009176, 0.5266346 , 0.60007906]], dtype=float32)
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"""
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def __init__(
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self,
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env_fns: Iterator[callable],
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observation_space: Space = None,
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action_space: Space = None,
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copy: bool = True,
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):
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"""Vectorized environment that serially runs multiple environments.
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Args:
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env_fns: iterable of callable functions that create the environments.
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observation_space: Observation space of a single environment. If ``None``, then the observation space of the first environment is taken.
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action_space: Action space of a single environment. If ``None``, then the action space of the first environment is taken.
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copy: If ``True``, then the :meth:`reset` and :meth:`step` methods return a copy of the observations.
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Raises:
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RuntimeError: If the observation space of some sub-environment does not match observation_space (or, by default, the observation space of the first sub-environment).
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"""
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self.env_fns = env_fns
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self.envs = [env_fn() for env_fn in env_fns]
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self.copy = copy
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self.metadata = self.envs[0].metadata
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if (observation_space is None) or (action_space is None):
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observation_space = observation_space or self.envs[0].observation_space
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action_space = action_space or self.envs[0].action_space
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super().__init__(
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num_envs=len(self.envs),
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observation_space=observation_space,
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action_space=action_space,
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)
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self._check_spaces()
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self.observations = create_empty_array(
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self.single_observation_space, n=self.num_envs, fn=np.zeros
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)
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self._rewards = np.zeros((self.num_envs,), dtype=np.float64)
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self._dones = np.zeros((self.num_envs,), dtype=np.bool_)
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self._actions = None
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def seed(self, seed: Optional[Union[int, Sequence[int]]] = None):
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"""Sets the seed in all sub-environments.
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Args:
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seed: The seed
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"""
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super().seed(seed=seed)
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if seed is None:
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seed = [None for _ in range(self.num_envs)]
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if isinstance(seed, int):
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seed = [seed + i for i in range(self.num_envs)]
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assert len(seed) == self.num_envs
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for env, single_seed in zip(self.envs, seed):
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env.seed(single_seed)
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def reset_wait(
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self,
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seed: Optional[Union[int, list[int]]] = None,
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return_info: bool = False,
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options: Optional[dict] = None,
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):
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"""Waits for the calls triggered by :meth:`reset_async` to finish and returns the results.
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Args:
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seed: The reset environment seed
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return_info: If to return information
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options: Option information for the environment reset
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Returns:
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The reset observation of the environment and reset information
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"""
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if seed is None:
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seed = [None for _ in range(self.num_envs)]
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if isinstance(seed, int):
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seed = [seed + i for i in range(self.num_envs)]
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assert len(seed) == self.num_envs
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self._dones[:] = False
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observations = []
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infos = {}
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for i, (env, single_seed) in enumerate(zip(self.envs, seed)):
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kwargs = {}
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if single_seed is not None:
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kwargs["seed"] = single_seed
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if options is not None:
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kwargs["options"] = options
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if return_info is True:
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kwargs["return_info"] = return_info
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if not return_info:
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observation = env.reset(**kwargs)
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observations.append(observation)
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else:
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observation, info = env.reset(**kwargs)
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observations.append(observation)
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infos = self._add_info(infos, info, i)
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self.observations = concatenate(
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self.single_observation_space, observations, self.observations
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)
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if not return_info:
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return deepcopy(self.observations) if self.copy else self.observations
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else:
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return (
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deepcopy(self.observations) if self.copy else self.observations
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), infos
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def step_async(self, actions):
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"""Sets :attr:`_actions` for use by the :meth:`step_wait` by converting the ``actions`` to an iterable version."""
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self._actions = iterate(self.action_space, actions)
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def step_wait(self):
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"""Steps through each of the environments returning the batched results.
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Returns:
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The batched environment step results
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"""
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observations, infos = [], {}
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for i, (env, action) in enumerate(zip(self.envs, self._actions)):
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observation, self._rewards[i], self._dones[i], info = env.step(action)
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if self._dones[i]:
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info["terminal_observation"] = observation
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observation = env.reset()
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observations.append(observation)
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infos = self._add_info(infos, info, i)
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self.observations = concatenate(
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self.single_observation_space, observations, self.observations
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)
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return (
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deepcopy(self.observations) if self.copy else self.observations,
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np.copy(self._rewards),
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np.copy(self._dones),
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infos,
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)
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def call(self, name, *args, **kwargs) -> tuple:
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"""Calls the method with name and applies args and kwargs.
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Args:
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name: The method name
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*args: The method args
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**kwargs: The method kwargs
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Returns:
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Tuple of results
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"""
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results = []
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for env in self.envs:
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function = getattr(env, name)
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if callable(function):
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results.append(function(*args, **kwargs))
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else:
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results.append(function)
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return tuple(results)
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def set_attr(self, name: str, values: Union[list, tuple, Any]):
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"""Sets an attribute of the sub-environments.
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Args:
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name: The property name to change
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values: Values of the property to be set to. If ``values`` is a list or
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tuple, then it corresponds to the values for each individual
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environment, otherwise, a single value is set for all environments.
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"""
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if not isinstance(values, (list, tuple)):
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values = [values for _ in range(self.num_envs)]
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if len(values) != self.num_envs:
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raise ValueError(
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"Values must be a list or tuple with length equal to the "
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f"number of environments. Got `{len(values)}` values for "
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f"{self.num_envs} environments."
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)
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for env, value in zip(self.envs, values):
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setattr(env, name, value)
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def close_extras(self, **kwargs):
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"""Close the environments."""
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[env.close() for env in self.envs]
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def _check_spaces(self) -> bool:
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for env in self.envs:
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if not (env.observation_space == self.single_observation_space):
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raise RuntimeError(
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"Some environments have an observation space different from "
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f"`{self.single_observation_space}`. In order to batch observations, "
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"the observation spaces from all environments must be equal."
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)
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if not (env.action_space == self.single_action_space):
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raise RuntimeError(
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"Some environments have an action space different from "
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f"`{self.single_action_space}`. In order to batch actions, the "
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"action spaces from all environments must be equal."
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)
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return True
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