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https://github.com/Farama-Foundation/Gymnasium.git
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0263deb5ab
* Add support for python 3.6 * Add support for python 3.6 * Added check for python 3.6 to not install mujoco as no version exists * Fixed the install groups for python 3.6 * Re-added python 3.6 support for gym * black * Added support for dataclasses through dataclasses module in setup that backports the module * Fixed install requirements * Re-added dummy env spec with dataclasses * Changed type for compatability for python 3.6 * Added a python 3.6 warning * Fixed python 3.6 typing issue * Removed __future__ import annotation for python 3.6 support * Fixed python 3.6 typing
87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
"""Implementation of a space that consists of binary np.ndarrays of a fixed shape."""
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from typing import Optional, Sequence, Tuple, Union
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import numpy as np
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from gym.spaces.space import Space
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from gym.utils import seeding
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class MultiBinary(Space[np.ndarray]):
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"""An n-shape binary space.
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Elements of this space are binary arrays of a shape that is fixed during construction.
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Example Usage::
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>>> observation_space = MultiBinary(5)
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>>> observation_space.sample()
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array([0, 1, 0, 1, 0], dtype=int8)
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>>> observation_space = MultiBinary([3, 2])
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>>> observation_space.sample()
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array([[0, 0],
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[0, 1],
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[1, 1]], dtype=int8)
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"""
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def __init__(
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self,
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n: Union[np.ndarray, Sequence[int], int],
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seed: Optional[Union[int, seeding.RandomNumberGenerator]] = None,
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):
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"""Constructor of :class:`MultiBinary` space.
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Args:
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n: This will fix the shape of elements of the space. It can either be an integer (if the space is flat)
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or some sort of sequence (tuple, list or np.ndarray) if there are multiple axes.
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seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space.
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"""
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if isinstance(n, (Sequence, np.ndarray)):
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self.n = input_n = tuple(int(i) for i in n)
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assert (np.asarray(input_n) > 0).all() # n (counts) have to be positive
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else:
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self.n = n = int(n)
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input_n = (n,)
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assert (np.asarray(input_n) > 0).all() # n (counts) have to be positive
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super().__init__(input_n, np.int8, seed)
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@property
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def shape(self) -> Tuple[int, ...]:
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"""Has stricter type than gym.Space - never None."""
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return self._shape # type: ignore
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def sample(self) -> np.ndarray:
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"""Generates a single random sample from this space.
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A sample is drawn by independent, fair coin tosses (one toss per binary variable of the space).
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Returns:
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Sampled values from space
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"""
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return self.np_random.integers(low=0, high=2, size=self.n, dtype=self.dtype)
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def contains(self, x) -> bool:
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"""Return boolean specifying if x is a valid member of this space."""
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if isinstance(x, Sequence):
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x = np.array(x) # Promote list to array for contains check
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if self.shape != x.shape:
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return False
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return ((x == 0) | (x == 1)).all()
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def to_jsonable(self, sample_n) -> list:
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"""Convert a batch of samples from this space to a JSONable data type."""
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return np.array(sample_n).tolist()
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def from_jsonable(self, sample_n) -> list:
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"""Convert a JSONable data type to a batch of samples from this space."""
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return [np.asarray(sample) for sample in sample_n]
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def __repr__(self) -> str:
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"""Gives a string representation of this space."""
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return f"MultiBinary({self.n})"
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def __eq__(self, other) -> bool:
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"""Check whether `other` is equivalent to this instance."""
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return isinstance(other, MultiBinary) and self.n == other.n
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