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e913bc81b8
* feat: add `isort` to `pre-commit` * ci: skip `__init__.py` file for `isort` * ci: make `isort` mandatory in lint pipeline * docs: add a section on Git hooks * ci: check isort diff * fix: isort from master branch * docs: add pre-commit badge * ci: update black + bandit versions * feat: add PR template * refactor: PR template * ci: remove bandit * docs: add Black badge * ci: try to remove all `|| true` statements * ci: remove lint_python job - Remove `lint_python` CI job - Move `pyupgrade` job to `pre-commit` workflow * fix: avoid messing with typing * docs: add a note on running `pre-cpmmit` manually * ci: apply `pre-commit` to the whole codebase
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
from __future__ import annotations
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from typing import Generic, Iterable, Mapping, Optional, Sequence, Type, TypeVar
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import numpy as np
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from gym.utils import seeding
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T_cov = TypeVar("T_cov", covariant=True)
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class Space(Generic[T_cov]):
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"""Defines the observation and action spaces, so you can write generic
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code that applies to any Env. For example, you can choose a random
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action.
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WARNING - Custom observation & action spaces can inherit from the `Space`
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class. However, most use-cases should be covered by the existing space
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classes (e.g. `Box`, `Discrete`, etc...), and container classes (`Tuple` &
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`Dict`). Note that parametrized probability distributions (through the
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`sample()` method), and batching functions (in `gym.vector.VectorEnv`), are
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only well-defined for instances of spaces provided in gym by default.
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Moreover, some implementations of Reinforcement Learning algorithms might
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not handle custom spaces properly. Use custom spaces with care.
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"""
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def __init__(
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self,
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shape: Optional[Sequence[int]] = None,
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dtype: Optional[Type | str] = None,
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seed: Optional[int] = None,
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):
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self._shape = None if shape is None else tuple(shape)
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self.dtype = None if dtype is None else np.dtype(dtype)
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self._np_random = None
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if seed is not None:
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self.seed(seed)
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@property
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def np_random(self) -> seeding.RandomNumberGenerator:
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"""Lazily seed the rng since this is expensive and only needed if
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sampling from this space.
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"""
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if self._np_random is None:
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self.seed()
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return self._np_random # type: ignore ## self.seed() call guarantees right type.
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@property
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def shape(self) -> Optional[tuple[int, ...]]:
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"""Return the shape of the space as an immutable property"""
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return self._shape
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def sample(self) -> T_cov:
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"""Randomly sample an element of this space. Can be
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uniform or non-uniform sampling based on boundedness of space."""
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raise NotImplementedError
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def seed(self, seed: Optional[int] = None) -> list:
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"""Seed the PRNG of this space."""
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self._np_random, seed = seeding.np_random(seed)
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return [seed]
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def contains(self, x) -> bool:
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"""
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Return boolean specifying if x is a valid
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member of this space
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"""
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raise NotImplementedError
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def __contains__(self, x) -> bool:
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return self.contains(x)
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def __setstate__(self, state: Iterable | Mapping):
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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:
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# https://github.com/openai/gym/pull/2397 -- shape
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# https://github.com/openai/gym/pull/1913 -- np_random
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#
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if "shape" in state:
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state["_shape"] = state["shape"]
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del state["shape"]
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if "np_random" in state:
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state["_np_random"] = state["np_random"]
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del state["np_random"]
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# Update our state
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self.__dict__.update(state)
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def to_jsonable(self, sample_n: Sequence[T_cov]) -> list:
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"""Convert a batch of samples from this space to a JSONable data type."""
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# By default, assume identity is JSONable
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return list(sample_n)
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def from_jsonable(self, sample_n: list) -> list[T_cov]:
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"""Convert a JSONable data type to a batch of samples from this space."""
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# By default, assume identity is JSONable
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return sample_n
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