mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-08 00:38:17 +00:00
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
75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
from __future__ import annotations
|
|
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
|
|
from gym.spaces.space import Space
|
|
from gym.utils import seeding
|
|
|
|
|
|
class Discrete(Space[int]):
|
|
r"""A discrete space in :math:`\{ 0, 1, \dots, n-1 \}`.
|
|
|
|
A start value can be optionally specified to shift the range
|
|
to :math:`\{ a, a+1, \dots, a+n-1 \}`.
|
|
|
|
Example::
|
|
|
|
>>> Discrete(2) # {0, 1}
|
|
>>> Discrete(3, start=-1) # {-1, 0, 1}
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
n: int,
|
|
seed: Optional[int | seeding.RandomNumberGenerator] = None,
|
|
start: int = 0,
|
|
):
|
|
assert n > 0, "n (counts) have to be positive"
|
|
assert isinstance(start, (int, np.integer))
|
|
self.n = int(n)
|
|
self.start = int(start)
|
|
super().__init__((), np.int64, seed)
|
|
|
|
def sample(self) -> int:
|
|
return int(self.start + self.np_random.integers(self.n))
|
|
|
|
def contains(self, x) -> bool:
|
|
if isinstance(x, int):
|
|
as_int = x
|
|
elif isinstance(x, (np.generic, np.ndarray)) and (
|
|
x.dtype.char in np.typecodes["AllInteger"] and x.shape == ()
|
|
):
|
|
as_int = int(x) # type: ignore
|
|
else:
|
|
return False
|
|
return self.start <= as_int < self.start + self.n
|
|
|
|
def __repr__(self) -> str:
|
|
if self.start != 0:
|
|
return "Discrete(%d, start=%d)" % (self.n, self.start)
|
|
return "Discrete(%d)" % self.n
|
|
|
|
def __eq__(self, other) -> bool:
|
|
return (
|
|
isinstance(other, Discrete)
|
|
and self.n == other.n
|
|
and self.start == other.start
|
|
)
|
|
|
|
def __setstate__(self, state):
|
|
super().__setstate__(state)
|
|
|
|
# Don't mutate the original state
|
|
state = dict(state)
|
|
|
|
# Allow for loading of legacy states.
|
|
# See https://github.com/openai/gym/pull/2470
|
|
if "start" not in state:
|
|
state["start"] = 0
|
|
|
|
# Update our state
|
|
self.__dict__.update(state)
|