Gymnasium/gym/vector/sync_vector_env.py

"""A synchronous vector environment."""
from __future__ import annotations

from copy import deepcopy
from typing import Any, Iterator, Optional, Sequence, Union

import numpy as np

from gym.spaces import Space
from gym.vector.utils import concatenate, create_empty_array, iterate
from gym.vector.vector_env import VectorEnv

__all__ = ["SyncVectorEnv"]


class SyncVectorEnv(VectorEnv):
    """Vectorized environment that serially runs multiple environments.

    Example::

        >>> import gym
        >>> env = gym.vector.SyncVectorEnv([
        ...     lambda: gym.make("Pendulum-v0", g=9.81),
        ...     lambda: gym.make("Pendulum-v0", g=1.62)
        ... ])
        >>> env.reset()
        array([[-0.8286432 ,  0.5597771 ,  0.90249056],
               [-0.85009176,  0.5266346 ,  0.60007906]], dtype=float32)
    """

    def __init__(
        self,
        env_fns: Iterator[callable],
        observation_space: Space = None,
        action_space: Space = None,
        copy: bool = True,
    ):
        """Vectorized environment that serially runs multiple environments.

        Args:
            env_fns: iterable of callable functions that create the environments.
            observation_space: Observation space of a single environment. If ``None``, then the observation space of the first environment is taken.
            action_space: Action space of a single environment. If ``None``, then the action space of the first environment is taken.
            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).
        """
        self.env_fns = env_fns
        self.envs = [env_fn() for env_fn in env_fns]
        self.copy = copy
        self.metadata = self.envs[0].metadata

        if (observation_space is None) or (action_space is None):
            observation_space = observation_space or self.envs[0].observation_space
            action_space = action_space or self.envs[0].action_space
        super().__init__(
            num_envs=len(self.envs),
            observation_space=observation_space,
            action_space=action_space,
        )

        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._dones = np.zeros((self.num_envs,), dtype=np.bool_)
        self._actions = None

    def seed(self, seed: Optional[Union[int, Sequence[int]]] = None):
        """Sets the seed in all sub-environments.

        Args:
            seed: The seed
        """
        super().seed(seed=seed)
        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

        for env, single_seed in zip(self.envs, seed):
            env.seed(single_seed)

    def reset_wait(
        self,
        seed: Optional[Union[int, list[int]]] = None,
        return_info: bool = False,
        options: Optional[dict] = None,
    ):
        """Waits for the calls triggered by :meth:`reset_async` to finish and returns the results.

        Args:
            seed: The reset environment seed
            return_info: If to return information
            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._dones[:] = False
        observations = []
        data_list = []
        for env, single_seed in zip(self.envs, seed):

            kwargs = {}
            if single_seed is not None:
                kwargs["seed"] = single_seed
            if options is not None:
                kwargs["options"] = options
            if return_info is True:
                kwargs["return_info"] = return_info

            if not return_info:
                observation = env.reset(**kwargs)
                observations.append(observation)
            else:
                observation, data = env.reset(**kwargs)
                observations.append(observation)
                data_list.append(data)

        self.observations = concatenate(
            self.single_observation_space, observations, self.observations
        )
        if not return_info:
            return deepcopy(self.observations) if self.copy else self.observations
        else:
            return (
                deepcopy(self.observations) if self.copy else self.observations
            ), data_list

    def step_async(self, actions):
        """Sets :attr:`_actions` for use by the :meth:`step_wait` by converting the ``actions`` to an iterable version."""
        self._actions = iterate(self.action_space, actions)

    def step_wait(self):
        """Steps through each of the environments returning the batched results.

        Returns:
            The batched environment step results
        """
        observations, infos = [], []
        for i, (env, action) in enumerate(zip(self.envs, self._actions)):
            observation, self._rewards[i], self._dones[i], info = env.step(action)
            if self._dones[i]:
                info["terminal_observation"] = observation
                observation = env.reset()
            observations.append(observation)
            infos.append(info)
        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._dones),
            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 set_attr(self, name: str, values: Union[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.
        """
        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