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134de4a713
* Updated testing requirements based off extra["testing"] * Updated setup to check the version is valid, added testing and all dependency groups and collects the requirements from requirements.txt to keep everything standardized. * Updated requirements.txt based on the current minimum gym requirements.txt to work * Updated requirements.txt based on the current minimum gym requirements.txt to work * Updated test_requirements.txt based on the current gym full testing requirements * Pre-commit updates * Add integer check for the `n` parameter * The type of self.spaces is an Iterable which is absorbed by the tuple. * Simplifies the environment checker to two files, env_checker.py and passive_env_checker.py with a new wrapper env_checker.py * Adds the passive environment checker on `gym.make` * Ignore the `check_env` warn parameter * Ignore the `check_env` warn parameter * Use the `data_equivalence` function * Revert rewrite setup.py changes * Remove smart formatting for 3.6 support * Fixed `check_action_space` and `check_observation_space` * Added disable_env_checker to vector.make such that env_checker would only run on the first environment created. * Removing check that different seeds would produce different initialising states * Use the unwrapped environment np_random * Fixed vector environment creator
99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
"""Implementation of a space consisting of finitely many elements."""
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from typing import Optional, 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 Discrete(Space[int]):
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r"""A space consisting of finitely many elements.
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This class represents a finite subset of integers, more specifically a set of the form :math:`\{ a, a+1, \dots, a+n-1 \}`.
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Example::
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>>> Discrete(2) # {0, 1}
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>>> Discrete(3, start=-1) # {-1, 0, 1}
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"""
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def __init__(
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self,
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n: int,
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seed: Optional[Union[int, seeding.RandomNumberGenerator]] = None,
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start: int = 0,
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):
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r"""Constructor of :class:`Discrete` space.
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This will construct the space :math:`\{\text{start}, ..., \text{start} + n - 1\}`.
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Args:
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n (int): The number of elements of this space.
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seed: Optionally, you can use this argument to seed the RNG that is used to sample from the ``Dict`` space.
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start (int): The smallest element of this space.
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"""
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assert isinstance(n, (int, np.integer))
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assert n > 0, "n (counts) have to be positive"
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assert isinstance(start, (int, np.integer))
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self.n = int(n)
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self.start = int(start)
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super().__init__((), np.int64, seed)
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def sample(self) -> int:
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"""Generates a single random sample from this space.
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A sample will be chosen uniformly at random.
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Returns:
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A sampled integer from the space
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"""
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return int(self.start + self.np_random.integers(self.n))
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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, int):
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as_int = x
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elif isinstance(x, (np.generic, np.ndarray)) and (
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x.dtype.char in np.typecodes["AllInteger"] and x.shape == ()
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):
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as_int = int(x) # type: ignore
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else:
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return False
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return self.start <= as_int < self.start + self.n
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def __repr__(self) -> str:
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"""Gives a string representation of this space."""
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if self.start != 0:
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return "Discrete(%d, start=%d)" % (self.n, self.start)
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return "Discrete(%d)" % 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 (
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isinstance(other, Discrete)
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and self.n == other.n
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and self.start == other.start
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)
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def __setstate__(self, state):
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"""Used when loading a pickled space.
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This method has to be implemented explicitly to allow for loading of legacy states.
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Args:
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state: The new state
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"""
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super().__setstate__(state)
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# Don't mutate the original state
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state = dict(state)
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# Allow for loading of legacy states.
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# See https://github.com/openai/gym/pull/2470
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if "start" not in state:
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state["start"] = 0
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# Update our state
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self.__dict__.update(state)
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