Source code for gymnasium.spaces.tuple

"""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 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 | 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. * ``List`` - 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