Gymnasium/gymnasium/spaces/space.py

"""Implementation of the `Space` metaclass."""

from typing import (
    Any,
    Generic,
    Iterable,
    List,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    TypeVar,
    Union,
)

import numpy as np

from gymnasium.utils import seeding

T_cov = TypeVar("T_cov", covariant=True)


class Space(Generic[T_cov]):
    """Superclass that is used to define observation and action spaces.

    Spaces are crucially used in Gym to define the format of valid actions and observations.
    They serve various purposes:

    * They clearly define how to interact with environments, i.e. they specify what actions need to look like
      and what observations will look like
    * They allow us to work with highly structured data (e.g. in the form of elements of :class:`Dict` spaces)
      and painlessly transform them into flat arrays that can be used in learning code
    * They provide a method to sample random elements. This is especially useful for exploration and debugging.

    Different spaces can be combined hierarchically via container spaces (:class:`Tuple` and :class:`Dict`) to build a
    more expressive space

    Warning:
        Custom observation & action spaces can inherit from the ``Space``
        class. However, most use-cases should be covered by the existing space
        classes (e.g. :class:`Box`, :class:`Discrete`, etc...), and container classes (:class`Tuple` &
        :class:`Dict`). Note that parametrized probability distributions (through the
        :meth:`Space.sample()` method), and batching functions (in :class:`gymnasium.vector.VectorEnv`), are
        only well-defined for instances of spaces provided in gymnasium by default.
        Moreover, some implementations of Reinforcement Learning algorithms might
        not handle custom spaces properly. Use custom spaces with care.
    """

    def __init__(
        self,
        shape: Optional[Sequence[int]] = None,
        dtype: Optional[Union[Type, str, np.dtype]] = None,
        seed: Optional[Union[int, np.random.Generator]] = None,
    ):
        """Constructor of :class:`Space`.

        Args:
            shape (Optional[Sequence[int]]): If elements of the space are numpy arrays, this should specify their shape.
            dtype (Optional[Type | str]): If elements of the space are numpy arrays, this should specify their dtype.
            seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space
        """
        self._shape = None if shape is None else tuple(shape)
        self.dtype = None if dtype is None else np.dtype(dtype)
        self._np_random = None
        if seed is not None:
            if isinstance(seed, np.random.Generator):
                self._np_random = seed
            else:
                self.seed(seed)

    @property
    def np_random(self) -> np.random.Generator:
        """Lazily seed the PRNG since this is expensive and only needed if sampling from this space."""
        if self._np_random is None:
            self.seed()

        return self._np_random  # type: ignore  ## self.seed() call guarantees right type.

    @property
    def shape(self) -> Optional[Tuple[int, ...]]:
        """Return the shape of the space as an immutable property."""
        return self._shape

    @property
    def is_np_flattenable(self):
        """Checks whether this space can be flattened to a :class:`spaces.Box`."""
        raise NotImplementedError

    def sample(self, mask: Optional[Any] = None) -> T_cov:
        """Randomly sample an element of this space.

        Can be uniform or non-uniform sampling based on boundedness of space.

        Args:
            mask: A mask used for sampling, expected ``dtype=np.int8`` and see sample implementation for expected shape.

        Returns:
            A sampled actions from the space
        """
        raise NotImplementedError

    def seed(self, seed: Optional[int] = None) -> list:
        """Seed the PRNG of this space and possibly the PRNGs of subspaces."""
        self._np_random, seed = seeding.np_random(seed)
        return [seed]

    def contains(self, x) -> bool:
        """Return boolean specifying if x is a valid member of this space."""
        raise NotImplementedError

    def __contains__(self, x) -> bool:
        """Return boolean specifying if x is a valid member of this space."""
        return self.contains(x)

    def __setstate__(self, state: Union[Iterable, Mapping]):
        """Used when loading a pickled space.

        This method was implemented explicitly to allow for loading of legacy states.

        Args:
            state: The updated state value
        """
        # Don't mutate the original state
        state = dict(state)

        # Allow for loading of legacy states.
        # See:
        #   https://github.com/openai/gym/pull/2397 -- shape
        #   https://github.com/openai/gym/pull/1913 -- np_random
        #
        if "shape" in state:
            state["_shape"] = state["shape"]
            del state["shape"]
        if "np_random" in state:
            state["_np_random"] = state["np_random"]
            del state["np_random"]

        # Update our state
        self.__dict__.update(state)

    def to_jsonable(self, sample_n: Sequence[T_cov]) -> list:
        """Convert a batch of samples from this space to a JSONable data type."""
        # By default, assume identity is JSONable
        return list(sample_n)

    def from_jsonable(self, sample_n: list) -> List[T_cov]:
        """Convert a JSONable data type to a batch of samples from this space."""
        # By default, assume identity is JSONable
        return sample_n