"""Implementation of the `Space` metaclass."""
from __future__ import annotations
from typing import Any, Generic, Iterable, Mapping, Sequence, TypeVar
import numpy as np
import numpy.typing as npt
from gymnasium.utils import seeding
T_cov = TypeVar("T_cov", covariant=True)
MaskNDArray = npt.NDArray[np.int8]
[docs]
class Space(Generic[T_cov]):
"""Superclass that is used to define observation and action spaces.
Spaces are crucially used in Gym to define the format of valid actions and observations.
They serve various purposes:
* They clearly define how to interact with environments, i.e. they specify what actions need to look like
and what observations will look like
* They allow us to work with highly structured data (e.g. in the form of elements of :class:`Dict` spaces)
and painlessly transform them into flat arrays that can be used in learning code
* They provide a method to sample random elements. This is especially useful for exploration and debugging.
Different spaces can be combined hierarchically via container spaces (:class:`Tuple` and :class:`Dict`) to build a
more expressive space
Warning:
Custom observation & action spaces can inherit from the ``Space``
class. However, most use-cases should be covered by the existing space
classes (e.g. :class:`Box`, :class:`Discrete`, etc...), and container classes (:class:`Tuple` &
:class:`Dict`). Note that parametrized probability distributions (through the
:meth:`Space.sample()` method), and batching functions (in :class:`gym.vector.VectorEnv`), are
only well-defined for instances of spaces provided in gym by default.
Moreover, some implementations of Reinforcement Learning algorithms might
not handle custom spaces properly. Use custom spaces with care.
"""
def __init__(
self,
shape: Sequence[int] | None = None,
dtype: npt.DTypeLike | None = None,
seed: int | np.random.Generator | None = None,
):
"""Constructor of :class:`Space`.
Args:
shape (Optional[Sequence[int]]): If elements of the space are numpy arrays, this should specify their shape.
dtype (Optional[Type | str]): If elements of the space are numpy arrays, this should specify their dtype.
seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space
"""
self._shape = None if shape is None else tuple(shape)
self.dtype = None if dtype is None else np.dtype(dtype)
self._np_random = None
if seed is not None:
if isinstance(seed, np.random.Generator):
self._np_random = seed
else:
self.seed(seed)
@property
def np_random(self) -> np.random.Generator:
"""Lazily seed the PRNG since this is expensive and only needed if sampling from this space.
As :meth:`seed` is not guaranteed to set the `_np_random` for particular seeds. We add a
check after :meth:`seed` to set a new random number generator.
"""
if self._np_random is None:
self.seed()
# As `seed` is not guaranteed (in particular for composite spaces) to set the `_np_random` then we set it randomly.
if self._np_random is None:
self._np_random, _ = seeding.np_random()
return self._np_random
@property
def shape(self) -> tuple[int, ...] | None:
"""Return the shape of the space as an immutable property."""
return self._shape
@property
def is_np_flattenable(self) -> bool:
"""Checks whether this space can be flattened to a :class:`gymnasium.spaces.Box`."""
raise NotImplementedError
[docs]
def sample(self, mask: Any | None = None) -> T_cov:
"""Randomly sample an element of this space.
Can be uniform or non-uniform sampling based on boundedness of space.
Args:
mask: A mask used for sampling, expected ``dtype=np.int8`` and see sample implementation for expected shape.
Returns:
A sampled actions from the space
"""
raise NotImplementedError
[docs]
def seed(self, seed: int | None = None) -> int | list[int] | dict[str, int]:
"""Seed the pseudorandom number generator (PRNG) of this space and, if applicable, the PRNGs of subspaces.
Args:
seed: The seed value for the space. This is expanded for composite spaces to accept multiple values. For further details, please refer to the space's documentation.
Returns:
The seed values used for all the PRNGs, for composite spaces this can be a tuple or dictionary of values.
"""
self._np_random, np_random_seed = seeding.np_random(seed)
return np_random_seed
[docs]
def contains(self, x: Any) -> bool:
"""Return boolean specifying if x is a valid member of this space, equivalent to ``sample in space``."""
raise NotImplementedError
def __contains__(self, x: Any) -> bool:
"""Return boolean specifying if x is a valid member of this space."""
return self.contains(x)
def __setstate__(self, state: Iterable[tuple[str, Any]] | Mapping[str, Any]):
"""Used when loading a pickled space.
This method was implemented explicitly to allow for loading of legacy states.
Args:
state: The updated state value
"""
# Don't mutate the original state
state = dict(state)
# Allow for loading of legacy states.
# See:
# https://github.com/openai/gym/pull/2397 -- shape
# https://github.com/openai/gym/pull/1913 -- np_random
#
if "shape" in state:
state["_shape"] = state.get("shape")
del state["shape"]
if "np_random" in state:
state["_np_random"] = state["np_random"]
del state["np_random"]
# Update our state
self.__dict__.update(state)
[docs]
def to_jsonable(self, sample_n: Sequence[T_cov]) -> list[Any]:
"""Convert a batch of samples from this space to a JSONable data type."""
# By default, assume identity is JSONable
return list(sample_n)
[docs]
def from_jsonable(self, sample_n: list[Any]) -> list[T_cov]:
"""Convert a JSONable data type to a batch of samples from this space."""
# By default, assume identity is JSONable
return sample_n
