"""A synchronous vector environment."""
from __future__ import annotations
from copy import deepcopy
from typing import Any, Callable, Iterator
import numpy as np
from gymnasium import Env
from gymnasium.experimental.vector.utils import (
batch_space,
concatenate,
create_empty_array,
iterate,
)
from gymnasium.experimental.vector.vector_env import VectorEnv
__all__ = ["SyncVectorEnv"]
[docs]class SyncVectorEnv(VectorEnv):
"""Vectorized environment that serially runs multiple environments.
Example:
>>> import gymnasium as gym
>>> env = gym.vector.SyncVectorEnv([
... lambda: gym.make("Pendulum-v1", g=9.81),
... lambda: gym.make("Pendulum-v1", g=1.62)
... ])
>>> env.reset(seed=42)
(array([[-0.14995256, 0.9886932 , -0.12224312],
[ 0.5760367 , 0.8174238 , -0.91244936]], dtype=float32), {})
"""
def __init__(
self,
env_fns: Iterator[Callable[[], Env]],
copy: bool = True,
):
"""Vectorized environment that serially runs multiple environments.
Args:
env_fns: iterable of callable functions that create the environments.
copy: If ``True``, then the :meth:`reset` and :meth:`step` methods return a copy of the observations.
Raises:
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).
"""
super().__init__()
self.env_fns = env_fns
self.envs = [env_fn() for env_fn in env_fns]
self.num_envs = len(self.envs)
self.copy = copy
self.metadata = self.envs[0].metadata
self.spec = self.envs[0].spec
self.single_observation_space = self.envs[0].observation_space
self.single_action_space = self.envs[0].action_space
self.observation_space = batch_space(
self.single_observation_space, self.num_envs
)
self.action_space = batch_space(self.single_action_space, self.num_envs)
self._check_spaces()
self.observations = create_empty_array(
self.single_observation_space, n=self.num_envs, fn=np.zeros
)
self._rewards = np.zeros((self.num_envs,), dtype=np.float64)
self._terminateds = np.zeros((self.num_envs,), dtype=np.bool_)
self._truncateds = np.zeros((self.num_envs,), dtype=np.bool_)
def reset(
self,
seed: int | list[int] | None = None,
options: dict | None = None,
):
"""Waits for the calls triggered by :meth:`reset_async` to finish and returns the results.
Args:
seed: The reset environment seed
options: Option information for the environment reset
Returns:
The reset observation of the environment and reset information
"""
if seed is None:
seed = [None for _ in range(self.num_envs)]
if isinstance(seed, int):
seed = [seed + i for i in range(self.num_envs)]
assert len(seed) == self.num_envs
self._terminateds[:] = False
self._truncateds[:] = False
observations = []
infos = {}
for i, (env, single_seed) in enumerate(zip(self.envs, seed)):
kwargs = {}
if single_seed is not None:
kwargs["seed"] = single_seed
if options is not None:
kwargs["options"] = options
observation, info = env.reset(**kwargs)
observations.append(observation)
infos = self._add_info(infos, info, i)
self.observations = concatenate(
self.single_observation_space, observations, self.observations
)
return (deepcopy(self.observations) if self.copy else self.observations), infos
def step(self, actions):
"""Steps through each of the environments returning the batched results.
Returns:
The batched environment step results
"""
actions = iterate(self.action_space, actions)
observations, infos = [], {}
for i, (env, action) in enumerate(zip(self.envs, actions)):
(
observation,
self._rewards[i],
self._terminateds[i],
self._truncateds[i],
info,
) = env.step(action)
if self._terminateds[i] or self._truncateds[i]:
old_observation, old_info = observation, info
observation, info = env.reset()
info["final_observation"] = old_observation
info["final_info"] = old_info
observations.append(observation)
infos = self._add_info(infos, info, i)
self.observations = concatenate(
self.single_observation_space, observations, self.observations
)
return (
deepcopy(self.observations) if self.copy else self.observations,
np.copy(self._rewards),
np.copy(self._terminateds),
np.copy(self._truncateds),
infos,
)
def call(self, name, *args, **kwargs) -> tuple:
"""Calls the method with name and applies args and kwargs.
Args:
name: The method name
*args: The method args
**kwargs: The method kwargs
Returns:
Tuple of results
"""
results = []
for env in self.envs:
function = getattr(env, name)
if callable(function):
results.append(function(*args, **kwargs))
else:
results.append(function)
return tuple(results)
def get_attr(self, name: str):
"""Get a property from each parallel environment.
Args:
name (str): Name of the property to be get from each individual environment.
Returns:
The property with name
"""
return self.call(name)
def set_attr(self, name: str, values: list | tuple | Any):
"""Sets an attribute of the sub-environments.
Args:
name: The property name to change
values: Values of the property to be set to. If ``values`` is a list or
tuple, then it corresponds to the values for each individual
environment, otherwise, a single value is set for all environments.
Raises:
ValueError: Values must be a list or tuple with length equal to the number of environments.
"""
if not isinstance(values, (list, tuple)):
values = [values for _ in range(self.num_envs)]
if len(values) != self.num_envs:
raise ValueError(
"Values must be a list or tuple with length equal to the "
f"number of environments. Got `{len(values)}` values for "
f"{self.num_envs} environments."
)
for env, value in zip(self.envs, values):
setattr(env, name, value)
def close_extras(self, **kwargs):
"""Close the environments."""
[env.close() for env in self.envs]
def _check_spaces(self) -> bool:
for env in self.envs:
if not (env.observation_space == self.single_observation_space):
raise RuntimeError(
"Some environments have an observation space different from "
f"`{self.single_observation_space}`. In order to batch observations, "
"the observation spaces from all environments must be equal."
)
if not (env.action_space == self.single_action_space):
raise RuntimeError(
"Some environments have an action space different from "
f"`{self.single_action_space}`. In order to batch actions, the "
"action spaces from all environments must be equal."
)
return True
