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https://github.com/Farama-Foundation/Gymnasium.git
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c364506710
* Ditch most of the seeding.py and replace np_random with the numpy default_rng. Let's see if tests pass * Updated a bunch of RNG calls from the RandomState API to Generator API * black; didn't expect that, did ya? * Undo a typo * blaaack * More typo fixes * Fixed setting/getting state in multidiscrete spaces * Fix typo, fix a test to work with the new sampling * Correctly (?) pass the randomly generated seed if np_random is called with None as seed * Convert the Discrete sample to a python int (as opposed to np.int64) * Remove some redundant imports * First version of the compatibility layer for old-style RNG. Mainly to trigger tests. * Removed redundant f-strings * Style fixes, removing unused imports * Try to make tests pass by removing atari from the dockerfile * Try to make tests pass by removing atari from the setup * Try to make tests pass by removing atari from the setup * Try to make tests pass by removing atari from the setup * First attempt at deprecating `env.seed` and supporting `env.reset(seed=seed)` instead. Tests should hopefully pass but throw up a million warnings. * black; didn't expect that, didya? * Rename the reset parameter in VecEnvs back to `seed` * Updated tests to use the new seeding method * Removed a bunch of old `seed` calls. Fixed a bug in AsyncVectorEnv * Stop Discrete envs from doing part of the setup (and using the randomness) in init (as opposed to reset) * Add explicit seed to wrappers reset * Remove an accidental return * Re-add some legacy functions with a warning. * Use deprecation instead of regular warnings for the newly deprecated methods/functions
58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
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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self.n = n
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if type(n) in [tuple, list, np.ndarray]:
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input_n = n
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else:
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input_n = (n,)
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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, list) or isinstance(x, tuple):
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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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