Gymnasium/gym/spaces/multi_binary.py

"""Implementation of a space that consists of binary np.ndarrays of a fixed shape."""
from __future__ import annotations

from typing import Optional, Sequence, Union

import numpy as np

from gym.spaces.space import Space
from gym.utils import seeding


class MultiBinary(Space[np.ndarray]):
    """An n-shape binary space.

    Elements of this space are binary arrays of a shape that is fixed during construction.

    Example Usage::

        >>> observation_space = MultiBinary(5)
        >>> observation_space.sample()
            array([0, 1, 0, 1, 0], dtype=int8)
        >>> observation_space = MultiBinary([3, 2])
        >>> observation_space.sample()
            array([[0, 0],
                [0, 1],
                [1, 1]], dtype=int8)
    """

    def __init__(
        self,
        n: Union[np.ndarray, Sequence[int], int],
        seed: Optional[int | seeding.RandomNumberGenerator] = None,
    ):
        """Constructor of :class:`MultiBinary` space.

        Args:
            n: This will fix the shape of elements of the space. It can either be an integer (if the space is flat)
                or some sort of sequence (tuple, list or np.ndarray) if there are multiple axes.
            seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space.
        """
        if isinstance(n, (Sequence, np.ndarray)):
            self.n = input_n = tuple(int(i) for i in n)
            assert (np.asarray(input_n) > 0).all()  # n (counts) have to be positive
        else:
            self.n = n = int(n)
            input_n = (n,)
            assert (np.asarray(input_n) > 0).all()  # n (counts) have to be positive

        super().__init__(input_n, np.int8, seed)

    @property
    def shape(self) -> tuple[int, ...]:
        """Has stricter type than gym.Space - never None."""
        return self._shape  # type: ignore

    def sample(self) -> np.ndarray:
        """Generates a single random sample from this space.

        A sample is drawn by independent, fair coin tosses (one toss per binary variable of the space).
        """
        return self.np_random.integers(low=0, high=2, size=self.n, dtype=self.dtype)

    def contains(self, x) -> bool:
        """Return boolean specifying if x is a valid member of this space."""
        if isinstance(x, Sequence):
            x = np.array(x)  # Promote list to array for contains check
        if self.shape != x.shape:
            return False
        return ((x == 0) | (x == 1)).all()

    def to_jsonable(self, sample_n) -> list:
        """Convert a batch of samples from this space to a JSONable data type."""
        return np.array(sample_n).tolist()

    def from_jsonable(self, sample_n) -> list:
        """Convert a JSONable data type to a batch of samples from this space."""
        return [np.asarray(sample) for sample in sample_n]

    def __repr__(self) -> str:
        """Gives a string representation of this space."""
        return f"MultiBinary({self.n})"

    def __eq__(self, other) -> bool:
        """Check whether `other` is equivalent to this instance."""
        return isinstance(other, MultiBinary) and self.n == other.n