"""Implementation of a space that represents the cartesian product of other spaces."""
from __future__ import annotations
import typing
from typing import Any, Iterable
import numpy as np
from gymnasium.spaces.space import Space
[docs]
class OneOf(Space[Any]):
"""An exclusive tuple (more precisely: the direct sum) of :class:`Space` instances.
Elements of this space are elements of one of the constituent spaces.
Example:
>>> from gymnasium.spaces import OneOf, Box, Discrete
>>> observation_space = OneOf((Discrete(2), Box(-1, 1, shape=(2,))), seed=123)
>>> observation_space.sample() # the first element is the space index (Box in this case) and the second element is the sample from Box
(0, 0)
>>> observation_space.sample() # this time the Discrete space was sampled as index=0
(1, array([-0.00711833, -0.7257502 ], dtype=float32))
>>> observation_space[0]
Discrete(2)
>>> observation_space[1]
Box(-1.0, 1.0, (2,), float32)
>>> len(observation_space)
2
"""
def __init__(
self,
spaces: Iterable[Space[Any]],
seed: int | typing.Sequence[int] | np.random.Generator | None = None,
):
r"""Constructor of :class:`OneOf` space.
The generated instance will represent the cartesian product :math:`\text{spaces}[0] \times ... \times \text{spaces}[-1]`.
Args:
spaces (Iterable[Space]): The spaces that are involved in the cartesian product.
seed: Optionally, you can use this argument to seed the RNGs of the ``spaces`` to ensure reproducible sampling.
"""
assert isinstance(spaces, Iterable), f"{spaces} is not an iterable"
self.spaces = tuple(spaces)
assert len(self.spaces) > 0, "Empty `OneOf` spaces are not supported."
for space in self.spaces:
assert isinstance(
space, Space
), f"{space} does not inherit from `gymnasium.Space`. Actual Type: {type(space)}"
super().__init__(None, None, seed)
@property
def is_np_flattenable(self):
"""Checks whether this space can be flattened to a :class:`spaces.Box`."""
return all(space.is_np_flattenable for space in self.spaces)
[docs]
def seed(self, seed: int | tuple[int, ...] | None = None) -> tuple[int, ...]:
"""Seed the PRNG of this space and all subspaces.
Depending on the type of seed, the subspaces will be seeded differently
* ``None`` - All the subspaces will use a random initial seed
* ``Int`` - The integer is used to seed the :class:`Tuple` space that is used to generate seed values for each of the subspaces. Warning, this does not guarantee unique seeds for all the subspaces.
* ``Tuple[int, ...]`` - Values used to seed the subspaces, first value seeds the OneOf and subsequent seed the subspaces. This allows the seeding of multiple composite subspaces ``[42, 54, ...]``.
Args:
seed: An optional int or tuple of ints to seed the OneOf space and subspaces. See above for more details.
Returns:
A tuple of ints used to seed the OneOf space and subspaces
"""
if seed is None:
super_seed = super().seed(None)
return (super_seed,) + tuple(space.seed(None) for space in self.spaces)
elif isinstance(seed, int):
super_seed = super().seed(seed)
subseeds = self.np_random.integers(
np.iinfo(np.int32).max, size=len(self.spaces)
)
# this is necessary such that after int or list/tuple seeding, the OneOf PRNG are equivalent
super().seed(seed)
return (super_seed,) + tuple(
space.seed(int(subseed))
for space, subseed in zip(self.spaces, subseeds)
)
elif isinstance(seed, (tuple, list)):
if len(seed) != len(self.spaces) + 1:
raise ValueError(
f"Expects that the subspaces of seeds equals the number of subspaces + 1. Actual length of seeds: {len(seed)}, length of subspaces: {len(self.spaces)}"
)
return (super().seed(seed[0]),) + tuple(
space.seed(subseed) for space, subseed in zip(self.spaces, seed[1:])
)
else:
raise TypeError(
f"Expected None, int, or tuple of ints, actual type: {type(seed)}"
)
[docs]
def sample(self, mask: tuple[Any | None, ...] | None = None) -> tuple[int, Any]:
"""Generates a single random sample inside this space.
This method draws independent samples from the subspaces.
Args:
mask: An optional tuple of optional masks for each of the subspace's samples,
expects the same number of masks as spaces
Returns:
Tuple of the subspace's samples
"""
subspace_idx = self.np_random.integers(0, len(self.spaces), dtype=np.int64)
subspace = self.spaces[subspace_idx]
if mask is not None:
assert isinstance(
mask, tuple
), f"Expected type of mask is tuple, actual type: {type(mask)}"
assert len(mask) == len(
self.spaces
), f"Expected length of mask is {len(self.spaces)}, actual length: {len(mask)}"
mask = mask[subspace_idx]
return subspace_idx, subspace.sample(mask=mask)
def contains(self, x: tuple[int, Any]) -> bool:
"""Return boolean specifying if x is a valid member of this space."""
# subspace_idx, subspace_value = x
return (
isinstance(x, tuple)
and len(x) == 2
and isinstance(x[0], (np.int64, int))
and 0 <= x[0] < len(self.spaces)
and self.spaces[x[0]].contains(x[1])
)
def __repr__(self) -> str:
"""Gives a string representation of this space."""
return "OneOf(" + ", ".join([str(s) for s in self.spaces]) + ")"
def to_jsonable(
self, sample_n: typing.Sequence[tuple[int, Any]]
) -> list[list[Any]]:
"""Convert a batch of samples from this space to a JSONable data type."""
return [
[int(i), self.spaces[i].to_jsonable([subsample])[0]]
for (i, subsample) in sample_n
]
def from_jsonable(self, sample_n: list[list[Any]]) -> list[tuple[Any, ...]]:
"""Convert a JSONable data type to a batch of samples from this space."""
return [
(
np.int64(space_idx),
self.spaces[space_idx].from_jsonable([jsonable_sample])[0],
)
for space_idx, jsonable_sample in sample_n
]
def __getitem__(self, index: int) -> Space[Any]:
"""Get the subspace at specific `index`."""
return self.spaces[index]
def __len__(self) -> int:
"""Get the number of subspaces that are involved in the cartesian product."""
return len(self.spaces)
def __eq__(self, other: Any) -> bool:
"""Check whether ``other`` is equivalent to this instance."""
return isinstance(other, OneOf) and self.spaces == other.spaces
