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18c8b988d4
* Type cast for `spaces.Dict` * Type cast for `spaces.Tuple` * Type cast for `spaces.Discrete` * Type cast for `spaces.MultiDiscrete` * Type cast for `spaces.MultiBinary`
62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
from collections.abc import Sequence
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import numpy as np
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from .space import Space
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class MultiBinary(Space):
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"""
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An n-shape binary space.
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The argument to MultiBinary defines n, which could be a number or a `list` of numbers.
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Example Usage:
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>> self.observation_space = spaces.MultiBinary(5)
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>> self.observation_space.sample()
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array([0, 1, 0, 1, 0], dtype=int8)
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>> self.observation_space = spaces.MultiBinary([3, 2])
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>> self.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__(self, n, seed=None):
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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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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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def sample(self):
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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):
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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):
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return np.array(sample_n).tolist()
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def from_jsonable(self, sample_n):
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return [np.asarray(sample) for sample in sample_n]
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def __repr__(self):
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return f"MultiBinary({self.n})"
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def __eq__(self, other):
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return isinstance(other, MultiBinary) and self.n == other.n
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