162 lines
6.3 KiB
Python
162 lines
6.3 KiB
Python
"""Implementation of a space that represents the cartesian product of other spaces."""
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from __future__ import annotations
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import collections.abc
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import typing
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from typing import Any, Iterable
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import numpy as np
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from gymnasium.spaces.space import Space
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class Tuple(Space[typing.Tuple[Any, ...]], typing.Sequence[Any]):
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"""A tuple (more precisely: the cartesian product) of :class:`Space` instances.
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Elements of this space are tuples of elements of the constituent spaces.
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Example:
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>>> from gymnasium.spaces import Tuple, Box, Discrete
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>>> observation_space = Tuple((Discrete(2), Box(-1, 1, shape=(2,))), seed=42)
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>>> observation_space.sample()
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(0, array([-0.3991573 , 0.21649833], dtype=float32))
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"""
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def __init__(
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self,
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spaces: Iterable[Space[Any]],
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seed: int | typing.Sequence[int] | np.random.Generator | None = None,
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):
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r"""Constructor of :class:`Tuple` space.
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The generated instance will represent the cartesian product :math:`\text{spaces}[0] \times ... \times \text{spaces}[-1]`.
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Args:
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spaces (Iterable[Space]): The spaces that are involved in the cartesian product.
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seed: Optionally, you can use this argument to seed the RNGs of the ``spaces`` to ensure reproducible sampling.
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"""
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self.spaces = tuple(spaces)
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for space in self.spaces:
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assert isinstance(
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space, Space
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), f"{space} does not inherit from `gymnasium.Space`. Actual Type: {type(space)}"
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super().__init__(None, None, seed) # type: ignore
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@property
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def is_np_flattenable(self):
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"""Checks whether this space can be flattened to a :class:`spaces.Box`."""
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return all(space.is_np_flattenable for space in self.spaces)
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def seed(self, seed: int | typing.Sequence[int] | None = None) -> list[int]:
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"""Seed the PRNG of this space and all subspaces.
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Depending on the type of seed, the subspaces will be seeded differently
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* ``None`` - All the subspaces will use a random initial seed
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* ``Int`` - The integer is used to seed the `Tuple` space that is used to generate seed values for each of the subspaces. Warning, this does not guarantee unique seeds for all of the subspaces.
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* ``List`` - Values used to seed the subspaces. This allows the seeding of multiple composite subspaces (``List(42, 54, ...``).
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Args:
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seed: An optional list of ints or int to seed the (sub-)spaces.
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"""
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seeds: list[int] = []
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if isinstance(seed, collections.abc.Sequence):
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assert len(seed) == len(
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self.spaces
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), f"Expects that the subspaces of seeds equals the number of subspaces. Actual length of seeds: {len(seeds)}, length of subspaces: {len(self.spaces)}"
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for subseed, space in zip(seed, self.spaces):
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seeds += space.seed(subseed)
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elif isinstance(seed, int):
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seeds = super().seed(seed)
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subseeds = self.np_random.integers(
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np.iinfo(np.int32).max, size=len(self.spaces)
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)
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for subspace, subseed in zip(self.spaces, subseeds):
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seeds += subspace.seed(int(subseed))
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elif seed is None:
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for space in self.spaces:
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seeds += space.seed(seed)
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else:
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raise TypeError(
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f"Expected seed type: list, tuple, int or None, actual type: {type(seed)}"
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)
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return seeds
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def sample(self, mask: tuple[Any | None, ...] | None = None) -> tuple[Any, ...]:
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"""Generates a single random sample inside this space.
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This method draws independent samples from the subspaces.
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Args:
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mask: An optional tuple of optional masks for each of the subspace's samples,
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expects the same number of masks as spaces
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Returns:
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Tuple of the subspace's samples
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"""
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if mask is not None:
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assert isinstance(
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mask, tuple
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), f"Expected type of mask is tuple, actual type: {type(mask)}"
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assert len(mask) == len(
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self.spaces
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), f"Expected length of mask is {len(self.spaces)}, actual length: {len(mask)}"
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return tuple(
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space.sample(mask=sub_mask)
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for space, sub_mask in zip(self.spaces, mask)
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)
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return tuple(space.sample() for space in self.spaces)
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def contains(self, x: Any) -> bool:
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"""Return boolean specifying if x is a valid member of this space."""
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if isinstance(x, (list, np.ndarray)):
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x = tuple(x) # Promote list and ndarray to tuple for contains check
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return (
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isinstance(x, tuple)
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and len(x) == len(self.spaces)
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and all(space.contains(part) for (space, part) in zip(self.spaces, x))
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)
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def __repr__(self) -> str:
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"""Gives a string representation of this space."""
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return "Tuple(" + ", ".join([str(s) for s in self.spaces]) + ")"
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def to_jsonable(
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self, sample_n: typing.Sequence[tuple[Any, ...]]
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) -> list[list[Any]]:
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"""Convert a batch of samples from this space to a JSONable data type."""
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# serialize as list-repr of tuple of vectors
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return [
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space.to_jsonable([sample[i] for sample in sample_n])
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for i, space in enumerate(self.spaces)
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]
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def from_jsonable(self, sample_n: list[list[Any]]) -> list[tuple[Any, ...]]:
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"""Convert a JSONable data type to a batch of samples from this space."""
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return [
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sample
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for sample in zip(
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*[
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space.from_jsonable(sample_n[i])
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for i, space in enumerate(self.spaces)
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]
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)
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]
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def __getitem__(self, index: int) -> Space[Any]:
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"""Get the subspace at specific `index`."""
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return self.spaces[index]
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def __len__(self) -> int:
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"""Get the number of subspaces that are involved in the cartesian product."""
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return len(self.spaces)
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def __eq__(self, other: Any) -> bool:
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"""Check whether ``other`` is equivalent to this instance."""
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return isinstance(other, Tuple) and self.spaces == other.spaces
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