mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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3354451300
* Add a case for the Box shape where the low and high values are both scalars
* Add seeding.RandomNumberGenerator parameter to Dict seed. Modify __repr__ for the dictionary space string looks similar to an actual dictionary
* Add seeding.RandomNumberGenerator parameter to Multi Binary seed
* Add seeding.RandomNumberGenerator parameter to Multi Binary seed. Modify nvec typing to include np.ndarray
* Space seed typing can be a seeding.RandomNumberGenerator. If a seeding.RNG is provided then it is assigned to _np_random and .seed is not run
* Fixed the tuple seeding type as List[int] is not a valid Space seed type
* Added typing to batch_space. The batch_space seed is equal to the space's seeding
* Fixed the seeding type
* Add test for batch space seeds are identical to the original space's seeding
* Add equivalence function for RandomNumberGenerator comparing the bit_generator.state
* The batch_space functions uses a copy of the seed for the original space
* Set the action space seed for sync_vector_env seed testing
* Add test for the seeding of the sync vector environment
* Update the test_batch_space_seed to check the resulting sampling are equivalent for testing
* Revert representation back to the original version
* Remove additional Box shape initialisation
* Remove additional typing of MultiDiscrete
* Fixed bug of Space batch space where the original space's np_random is not a complete copy of the original space
* Add CustomSpace to the batched space seed test
* Modify the CustomSpace sample to produce a random number not a static value
* Fix CustomSpace to reflect the sample function
* Copy the space.np_random for the batched_space seed to ensure that the original space doesn't sampling doesn't effect the batched_space
* Parameterized the batch_space_seed, added testing for rng_different_at_each_index and test_deterministic
* Black and isort pre-commit changes
* Pre-commit fix
* MacOS, test_read_from_shared_memory throws an error that the inner _process_write function was unpicklable. Making the function a top-level function solves this error
* Fixed typing of seed where a space's seed function differs from Space.seed's typing
* Added check that the sample lengths are equal and explicitly provided the number of batched spaces n=1
* Removed relative imports for absolute imports
* Use deepcopy instead of copy
* Replaces `from numpy.testing._private.utils import assert_array_equal` with `from numpy.testing import assert_array_equal`
* Using the seeding `__eq__` function, replace `np_random.bit_generator.state` with `np_random`
* Added docstrings and comments to the tests to explain their purpose
* Remove __eq__ from RandomNumberGenerator and add to tests/vector/utils
* Add sync vector determinism test for issue #2680
* Fixed bug for 462101d384 (r850740825)
* Made the new seeds a list of integers
105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
from __future__ import annotations
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from typing import Iterable, Optional, Sequence
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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 Tuple(Space[tuple], Sequence):
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"""
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A tuple (i.e., product) of simpler spaces
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Example usage::
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self.observation_space = spaces.Tuple((spaces.Discrete(2), spaces.Discrete(3)))
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"""
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def __init__(
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self,
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spaces: Iterable[Space],
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seed: Optional[int | list[int] | seeding.RandomNumberGenerator] = None,
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):
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spaces = tuple(spaces)
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self.spaces = spaces
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for space in spaces:
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assert isinstance(
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space, Space
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), "Elements of the tuple must be instances of gym.Space"
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super().__init__(None, None, seed) # type: ignore
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def seed(self, seed: Optional[int | list[int]] = None) -> list:
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seeds = []
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if isinstance(seed, list):
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for i, space in enumerate(self.spaces):
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seeds += space.seed(seed[i])
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elif isinstance(seed, int):
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seeds = super().seed(seed)
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try:
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subseeds = self.np_random.choice(
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np.iinfo(int).max,
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size=len(self.spaces),
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replace=False, # unique subseed for each subspace
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)
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except ValueError:
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subseeds = self.np_random.choice(
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np.iinfo(int).max,
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size=len(self.spaces),
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replace=True, # we get more than INT_MAX subspaces
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)
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for subspace, subseed in zip(self.spaces, subseeds):
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seeds.append(subspace.seed(int(subseed))[0])
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elif seed is None:
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for space in self.spaces:
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seeds += space.seed(seed)
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else:
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raise TypeError("Passed seed not of an expected type: list or int or None")
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return seeds
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def sample(self) -> tuple:
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return tuple(space.sample() for space in self.spaces)
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def contains(self, x) -> bool:
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if isinstance(x, (list, np.ndarray)):
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x = tuple(x) # Promote list and ndarray to tuple for contains check
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return (
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isinstance(x, tuple)
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and len(x) == len(self.spaces)
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and all(space.contains(part) for (space, part) in zip(self.spaces, x))
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)
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def __repr__(self) -> str:
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return "Tuple(" + ", ".join([str(s) for s in self.spaces]) + ")"
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def to_jsonable(self, sample_n: Sequence) -> list:
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# serialize as list-repr of tuple of vectors
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return [
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space.to_jsonable([sample[i] for sample in sample_n])
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for i, space in enumerate(self.spaces)
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]
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def from_jsonable(self, sample_n) -> list:
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return [
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sample
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for sample in zip(
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*[
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space.from_jsonable(sample_n[i])
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for i, space in enumerate(self.spaces)
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]
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)
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]
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def __getitem__(self, index: int) -> Space:
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return self.spaces[index]
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def __len__(self) -> int:
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return len(self.spaces)
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def __eq__(self, other) -> bool:
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return isinstance(other, Tuple) and self.spaces == other.spaces
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