"""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 Tuple(Space[typing.Tuple[Any, ...]], typing.Sequence[Any]):
"""A tuple (more precisely: the cartesian product) of :class:`Space` instances.
Elements of this space are tuples of elements of the constituent spaces.
Example:
>>> from gymnasium.spaces import Tuple, Box, Discrete
>>> observation_space = Tuple((Discrete(2), Box(-1, 1, shape=(2,))), seed=42)
>>> observation_space.sample()
(np.int64(0), array([-0.3991573 , 0.21649833], dtype=float32))
"""
def __init__(
self,
spaces: Iterable[Space[Any]],
seed: int | typing.Sequence[int] | np.random.Generator | None = None,
):
r"""Constructor of :class:`Tuple` 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.
"""
self.spaces = tuple(spaces)
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) # type: ignore
@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 | typing.Sequence[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.
* ``List`` / ``Tuple`` - Values used to seed the subspaces. This allows the seeding of multiple composite subspaces ``[42, 54, ...]``.
Args:
seed: An optional list of ints or int to seed the (sub-)spaces.
Returns:
A tuple of the seed values for all subspaces
"""
if seed is None:
return tuple(space.seed(None) for space in self.spaces)
elif isinstance(seed, int):
super().seed(seed)
subseeds = self.np_random.integers(
np.iinfo(np.int32).max, size=len(self.spaces)
)
return tuple(
subspace.seed(int(subseed))
for subspace, subseed in zip(self.spaces, subseeds)
)
elif isinstance(seed, (tuple, list)):
if len(seed) != len(self.spaces):
raise ValueError(
f"Expects that the subspaces of seeds equals the number of subspaces. Actual length of seeds: {len(seed)}, length of subspaces: {len(self.spaces)}"
)
return tuple(
space.seed(subseed) for subseed, space in zip(seed, self.spaces)
)
else:
raise TypeError(
f"Expected seed type: list, tuple, int or None, actual type: {type(seed)}"
)
[docs]
def sample(self, mask: tuple[Any | None, ...] | None = None) -> tuple[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
"""
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)}"
return tuple(
space.sample(mask=sub_mask)
for space, sub_mask in zip(self.spaces, mask)
)
return tuple(space.sample() for space in self.spaces)
def contains(self, x: Any) -> bool:
"""Return boolean specifying if x is a valid member of this space."""
if isinstance(x, (list, np.ndarray)):
x = tuple(x) # Promote list and ndarray to tuple for contains check
return (
isinstance(x, tuple)
and len(x) == len(self.spaces)
and all(space.contains(part) for (space, part) in zip(self.spaces, x))
)
def __repr__(self) -> str:
"""Gives a string representation of this space."""
return "Tuple(" + ", ".join([str(s) for s in self.spaces]) + ")"
def to_jsonable(
self, sample_n: typing.Sequence[tuple[Any, ...]]
) -> list[list[Any]]:
"""Convert a batch of samples from this space to a JSONable data type."""
# serialize as list-repr of tuple of vectors
return [
space.to_jsonable([sample[i] for sample in sample_n])
for i, space in enumerate(self.spaces)
]
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 [
sample
for sample in zip(
*[
space.from_jsonable(sample_n[i])
for i, space in enumerate(self.spaces)
]
)
]
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, Tuple) and self.spaces == other.spaces
