Gymnasium/gymnasium/vector/utils/numpy_utils.py

"""Numpy utility functions: concatenate space samples and create empty array."""
from collections import OrderedDict
from functools import singledispatch
from typing import Iterable, Union

import numpy as np

from gymnasium.spaces import (
    Box,
    Dict,
    Discrete,
    MultiBinary,
    MultiDiscrete,
    Space,
    Tuple,
)

__all__ = ["concatenate", "create_empty_array"]


@singledispatch
def concatenate(
    space: Space, items: Iterable, out: Union[tuple, dict, np.ndarray]
) -> Union[tuple, dict, np.ndarray]:
    """Concatenate multiple samples from space into a single object.

    Example::

        >>> from gymnasium.spaces import Box
        >>> space = Box(low=0, high=1, shape=(3,), dtype=np.float32)
        >>> out = np.zeros((2, 3), dtype=np.float32)
        >>> items = [space.sample() for _ in range(2)]
        >>> concatenate(space, items, out)
        array([[0.6348213 , 0.28607962, 0.60760117],
               [0.87383074, 0.192658  , 0.2148103 ]], dtype=float32)

    Args:
        space: Observation space of a single environment in the vectorized environment.
        items: Samples to be concatenated.
        out: The output object. This object is a (possibly nested) numpy array.

    Returns:
        The output object. This object is a (possibly nested) numpy array.

    Raises:
        ValueError: Space is not a valid :class:`gym.Space` instance
    """
    raise ValueError(
        f"Space of type `{type(space)}` is not a valid `gymnasium.Space` instance."
    )


@concatenate.register(Box)
@concatenate.register(Discrete)
@concatenate.register(MultiDiscrete)
@concatenate.register(MultiBinary)
def _concatenate_base(space, items, out):
    return np.stack(items, axis=0, out=out)


@concatenate.register(Tuple)
def _concatenate_tuple(space, items, out):
    return tuple(
        concatenate(subspace, [item[i] for item in items], out[i])
        for (i, subspace) in enumerate(space.spaces)
    )


@concatenate.register(Dict)
def _concatenate_dict(space, items, out):
    return OrderedDict(
        [
            (key, concatenate(subspace, [item[key] for item in items], out[key]))
            for (key, subspace) in space.spaces.items()
        ]
    )


@concatenate.register(Space)
def _concatenate_custom(space, items, out):
    return tuple(items)


@singledispatch
def create_empty_array(
    space: Space, n: int = 1, fn: callable = np.zeros
) -> Union[tuple, dict, np.ndarray]:
    """Create an empty (possibly nested) numpy array.

    Example::

        >>> from gymnasium.spaces import Box, Dict
        >>> space = Dict({
        ... 'position': Box(low=0, high=1, shape=(3,), dtype=np.float32),
        ... 'velocity': Box(low=0, high=1, shape=(2,), dtype=np.float32)})
        >>> create_empty_array(space, n=2, fn=np.zeros)
        OrderedDict([('position', array([[0., 0., 0.],
                                         [0., 0., 0.]], dtype=float32)),
                     ('velocity', array([[0., 0.],
                                         [0., 0.]], dtype=float32))])

    Args:
        space: Observation space of a single environment in the vectorized environment.
        n: Number of environments in the vectorized environment. If `None`, creates an empty sample from `space`.
        fn: Function to apply when creating the empty numpy array. Examples of such functions are `np.empty` or `np.zeros`.

    Returns:
        The output object. This object is a (possibly nested) numpy array.

    Raises:
        ValueError: Space is not a valid :class:`gym.Space` instance
    """
    raise ValueError(
        f"Space of type `{type(space)}` is not a valid `gymnasium.Space` instance."
    )


@create_empty_array.register(Box)
@create_empty_array.register(Discrete)
@create_empty_array.register(MultiDiscrete)
@create_empty_array.register(MultiBinary)
def _create_empty_array_base(space, n=1, fn=np.zeros):
    shape = space.shape if (n is None) else (n,) + space.shape
    return fn(shape, dtype=space.dtype)


@create_empty_array.register(Tuple)
def _create_empty_array_tuple(space, n=1, fn=np.zeros):
    return tuple(create_empty_array(subspace, n=n, fn=fn) for subspace in space.spaces)


@create_empty_array.register(Dict)
def _create_empty_array_dict(space, n=1, fn=np.zeros):
    return OrderedDict(
        [
            (key, create_empty_array(subspace, n=n, fn=fn))
            for (key, subspace) in space.spaces.items()
        ]
    )


@create_empty_array.register(Space)
def _create_empty_array_custom(space, n=1, fn=np.zeros):
    return None