"""Implementation of a space that represents closed boxes in euclidean space."""
from __future__ import annotations
from typing import Any, Iterable, Mapping, Sequence, SupportsFloat
import numpy as np
from numpy.typing import NDArray
import gymnasium as gym
from gymnasium.spaces.space import Space
def array_short_repr(arr: NDArray[Any]) -> str:
"""Create a shortened string representation of a numpy array.
If arr is a multiple of the all-ones vector, return a string representation of the multiplier.
Otherwise, return a string representation of the entire array.
Args:
arr: The array to represent
Returns:
A short representation of the array
"""
if arr.size != 0 and np.min(arr) == np.max(arr):
return str(np.min(arr))
return str(arr)
def is_float_integer(var: Any) -> bool:
"""Checks if a scalar variable is an integer or float (does not include bool)."""
return np.issubdtype(type(var), np.integer) or np.issubdtype(type(var), np.floating)
class Box(Space[NDArray[Any]]):
r"""A (possibly unbounded) box in :math:`\mathbb{R}^n`.
Specifically, a Box represents the Cartesian product of n closed intervals.
Each interval has the form of one of :math:`[a, b]`, :math:`(-\infty, b]`,
:math:`[a, \infty)`, or :math:`(-\infty, \infty)`.
There are two common use cases:
* Identical bound for each dimension::
>>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)
Box(-1.0, 2.0, (3, 4), float32)
* Independent bound for each dimension::
>>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)
Box([-1. -2.], [2. 4.], (2,), float32)
"""
def __init__(
self,
low: SupportsFloat | NDArray[Any],
high: SupportsFloat | NDArray[Any],
shape: Sequence[int] | None = None,
dtype: type[np.floating[Any]] | type[np.integer[Any]] = np.float32,
seed: int | np.random.Generator | None = None,
):
r"""Constructor of :class:`Box`.
The argument ``low`` specifies the lower bound of each dimension and ``high`` specifies the upper bounds.
I.e., the space that is constructed will be the product of the intervals :math:`[\text{low}[i], \text{high}[i]]`.
If ``low`` (or ``high``) is a scalar, the lower bound (or upper bound, respectively) will be assumed to be
this value across all dimensions.
Args:
low (SupportsFloat | np.ndarray): Lower bounds of the intervals. If integer, must be at least ``-2**63``.
high (SupportsFloat | np.ndarray]): Upper bounds of the intervals. If integer, must be at most ``2**63 - 2``.
shape (Optional[Sequence[int]]): The shape is inferred from the shape of `low` or `high` `np.ndarray`s with
`low` and `high` scalars defaulting to a shape of (1,)
dtype: The dtype of the elements of the space. If this is an integer type, the :class:`Box` is essentially a discrete space.
seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space.
Raises:
ValueError: If no shape information is provided (shape is None, low is None and high is None) then a
value error is raised.
"""
# determine dtype
if dtype is None:
raise ValueError("Box dtype must be explicitly provided, cannot be None.")
self.dtype = np.dtype(dtype)
# * check that dtype is an accepted dtype
if not (
np.issubdtype(self.dtype, np.integer)
or np.issubdtype(self.dtype, np.floating)
or self.dtype == np.bool_
):
raise ValueError(
f"Invalid Box dtype ({self.dtype}), must be an integer, floating, or bool dtype"
)
# determine shape
if shape is not None:
if not isinstance(shape, Iterable):
raise TypeError(
f"Expected Box shape to be an iterable, actual type={type(shape)}"
)
elif not all(np.issubdtype(type(dim), np.integer) for dim in shape):
raise TypeError(
f"Expected all Box shape elements to be integer, actual type={tuple(type(dim) for dim in shape)}"
)
# Casts the `shape` argument to tuple[int, ...] (otherwise dim can `np.int64`)
shape = tuple(int(dim) for dim in shape)
elif isinstance(low, np.ndarray) and isinstance(high, np.ndarray):
if low.shape != high.shape:
raise ValueError(
f"Box low.shape and high.shape don't match, low.shape={low.shape}, high.shape={high.shape}"
)
shape = low.shape
elif isinstance(low, np.ndarray):
shape = low.shape
elif isinstance(high, np.ndarray):
shape = high.shape
elif is_float_integer(low) and is_float_integer(high):
shape = (1,) # low and high are scalars
else:
raise ValueError(
"Box shape is not specified, therefore inferred from low and high. Expected low and high to be np.ndarray, integer, or float."
f"Actual types low={type(low)}, high={type(high)}"
)
self._shape: tuple[int, ...] = shape
# Cast scalar values to `np.ndarray` and capture the boundedness information
# disallowed cases
# * out of range - this must be done before casting to low and high otherwise, the value is within dtype and cannot be out of range
# * nan - must be done beforehand as int dtype can cast `nan` to another value
# * unsign int inf and -inf - special case that is disallowed
if self.dtype == np.bool_:
dtype_min, dtype_max = 0, 1
elif np.issubdtype(self.dtype, np.floating):
dtype_min = float(np.finfo(self.dtype).min)
dtype_max = float(np.finfo(self.dtype).max)
else:
dtype_min = int(np.iinfo(self.dtype).min)
dtype_max = int(np.iinfo(self.dtype).max)
# Cast `low` and `high` to ndarray for the dtype min and max for out of range tests
self.low, self.bounded_below = self._cast_low(low, dtype_min)
self.high, self.bounded_above = self._cast_high(high, dtype_max)
# recheck shape for case where shape and (low or high) are provided
if self.low.shape != shape:
raise ValueError(
f"Box low.shape doesn't match provided shape, low.shape={self.low.shape}, shape={self.shape}"
)
if self.high.shape != shape:
raise ValueError(
f"Box high.shape doesn't match provided shape, high.shape={self.high.shape}, shape={self.shape}"
)
# check that low <= high
if np.any(self.low > self.high):
raise ValueError(
f"Box all low values must be less than or equal to high (some values break this), low={self.low}, high={self.high}"
)
self.low_repr = array_short_repr(self.low)
self.high_repr = array_short_repr(self.high)
super().__init__(self.shape, self.dtype, seed)
def _cast_low(self, low, dtype_min) -> tuple[np.ndarray, np.ndarray]:
"""Casts the input Box low value to ndarray with provided dtype.
Args:
low: The input box low value
dtype_min: The dtype's minimum value
Returns:
The updated low value and for what values the input is bounded (below)
"""
if is_float_integer(low):
bounded_below = -np.inf < np.full(self.shape, low, dtype=float)
if np.isnan(low):
raise ValueError(f"No low value can be equal to `np.nan`, low={low}")
elif np.isneginf(low):
if self.dtype.kind == "i": # signed int
low = dtype_min
elif self.dtype.kind in {"u", "b"}: # unsigned int and bool
raise ValueError(
f"Box unsigned int dtype don't support `-np.inf`, low={low}"
)
elif low < dtype_min:
raise ValueError(
f"Box low is out of bounds of the dtype range, low={low}, min dtype={dtype_min}"
)
low = np.full(self.shape, low, dtype=self.dtype)
return low, bounded_below
else: # cast for low - array
if not isinstance(low, np.ndarray):
raise ValueError(
f"Box low must be a np.ndarray, integer, or float, actual type={type(low)}"
)
elif not (
np.issubdtype(low.dtype, np.floating)
or np.issubdtype(low.dtype, np.integer)
or low.dtype == np.bool_
):
raise ValueError(
f"Box low must be a floating, integer, or bool dtype, actual dtype={low.dtype}"
)
elif np.any(np.isnan(low)):
raise ValueError(f"No low value can be equal to `np.nan`, low={low}")
bounded_below = -np.inf < low
if np.any(np.isneginf(low)):
if self.dtype.kind == "i": # signed int
low[np.isneginf(low)] = dtype_min
elif self.dtype.kind in {"u", "b"}: # unsigned int and bool
raise ValueError(
f"Box unsigned int dtype don't support `-np.inf`, low={low}"
)
elif low.dtype != self.dtype and np.any(low < dtype_min):
raise ValueError(
f"Box low is out of bounds of the dtype range, low={low}, min dtype={dtype_min}"
)
if (
np.issubdtype(low.dtype, np.floating)
and np.issubdtype(self.dtype, np.floating)
and np.finfo(self.dtype).precision < np.finfo(low.dtype).precision
):
gym.logger.warn(
f"Box low's precision lowered by casting to {self.dtype}, current low.dtype={low.dtype}"
)
return low.astype(self.dtype), bounded_below
def _cast_high(self, high, dtype_max) -> tuple[np.ndarray, np.ndarray]:
"""Casts the input Box high value to ndarray with provided dtype.
Args:
high: The input box high value
dtype_max: The dtype's maximum value
Returns:
The updated high value and for what values the input is bounded (above)
"""
if is_float_integer(high):
bounded_above = np.full(self.shape, high, dtype=float) < np.inf
if np.isnan(high):
raise ValueError(f"No high value can be equal to `np.nan`, high={high}")
elif np.isposinf(high):
if self.dtype.kind == "i": # signed int
high = dtype_max
elif self.dtype.kind in {"u", "b"}: # unsigned int
raise ValueError(
f"Box unsigned int dtype don't support `np.inf`, high={high}"
)
elif high > dtype_max:
raise ValueError(
f"Box high is out of bounds of the dtype range, high={high}, max dtype={dtype_max}"
)
high = np.full(self.shape, high, dtype=self.dtype)
return high, bounded_above
else:
if not isinstance(high, np.ndarray):
raise ValueError(
f"Box high must be a np.ndarray, integer, or float, actual type={type(high)}"
)
elif not (
np.issubdtype(high.dtype, np.floating)
or np.issubdtype(high.dtype, np.integer)
or high.dtype == np.bool_
):
raise ValueError(
f"Box high must be a floating or integer dtype, actual dtype={high.dtype}"
)
elif np.any(np.isnan(high)):
raise ValueError(f"No high value can be equal to `np.nan`, high={high}")
bounded_above = high < np.inf
posinf = np.isposinf(high)
if np.any(posinf):
if self.dtype.kind == "i": # signed int
high[posinf] = dtype_max
elif self.dtype.kind in {"u", "b"}: # unsigned int
raise ValueError(
f"Box unsigned int dtype don't support `np.inf`, high={high}"
)
elif high.dtype != self.dtype and np.any(dtype_max < high):
raise ValueError(
f"Box high is out of bounds of the dtype range, high={high}, max dtype={dtype_max}"
)
if (
np.issubdtype(high.dtype, np.floating)
and np.issubdtype(self.dtype, np.floating)
and np.finfo(self.dtype).precision < np.finfo(high.dtype).precision
):
gym.logger.warn(
f"Box high's precision lowered by casting to {self.dtype}, current high.dtype={high.dtype}"
)
return high.astype(self.dtype), bounded_above
@property
def shape(self) -> tuple[int, ...]:
"""Has stricter type than gym.Space - never None."""
return self._shape
@property
def is_np_flattenable(self):
"""Checks whether this space can be flattened to a :class:`spaces.Box`."""
return True
[docs] def is_bounded(self, manner: str = "both") -> bool:
"""Checks whether the box is bounded in some sense.
Args:
manner (str): One of ``"both"``, ``"below"``, ``"above"``.
Returns:
If the space is bounded
Raises:
ValueError: If `manner` is neither ``"both"`` nor ``"below"`` or ``"above"``
"""
below = bool(np.all(self.bounded_below))
above = bool(np.all(self.bounded_above))
if manner == "both":
return below and above
elif manner == "below":
return below
elif manner == "above":
return above
else:
raise ValueError(
f"manner is not in {{'below', 'above', 'both'}}, actual value: {manner}"
)
def sample(self, mask: None = None) -> NDArray[Any]:
r"""Generates a single random sample inside the Box.
In creating a sample of the box, each coordinate is sampled (independently) from a distribution
that is chosen according to the form of the interval:
* :math:`[a, b]` : uniform distribution
* :math:`[a, \infty)` : shifted exponential distribution
* :math:`(-\infty, b]` : shifted negative exponential distribution
* :math:`(-\infty, \infty)` : normal distribution
Args:
mask: A mask for sampling values from the Box space, currently unsupported.
Returns:
A sampled value from the Box
"""
if mask is not None:
raise gym.error.Error(
f"Box.sample cannot be provided a mask, actual value: {mask}"
)
high = self.high if self.dtype.kind == "f" else self.high.astype("int64") + 1
sample = np.empty(self.shape)
# Masking arrays which classify the coordinates according to interval type
unbounded = ~self.bounded_below & ~self.bounded_above
upp_bounded = ~self.bounded_below & self.bounded_above
low_bounded = self.bounded_below & ~self.bounded_above
bounded = self.bounded_below & self.bounded_above
# Vectorized sampling by interval type
sample[unbounded] = self.np_random.normal(size=unbounded[unbounded].shape)
sample[low_bounded] = (
self.np_random.exponential(size=low_bounded[low_bounded].shape)
+ self.low[low_bounded]
)
sample[upp_bounded] = (
-self.np_random.exponential(size=upp_bounded[upp_bounded].shape)
+ high[upp_bounded]
)
sample[bounded] = self.np_random.uniform(
low=self.low[bounded], high=high[bounded], size=bounded[bounded].shape
)
if self.dtype.kind in ["i", "u", "b"]:
sample = np.floor(sample)
# clip values that would underflow/overflow
if np.issubdtype(self.dtype, np.signedinteger):
dtype_min = np.iinfo(self.dtype).min + 2
dtype_max = np.iinfo(self.dtype).max - 2
sample = sample.clip(min=dtype_min, max=dtype_max)
elif np.issubdtype(self.dtype, np.unsignedinteger):
dtype_min = np.iinfo(self.dtype).min
dtype_max = np.iinfo(self.dtype).max
sample = sample.clip(min=dtype_min, max=dtype_max)
sample = sample.astype(self.dtype)
# float64 values have lower than integer precision near int64 min/max, so clip
# again in case something has been cast to an out-of-bounds value
if self.dtype == np.int64:
sample = sample.clip(min=self.low, max=self.high)
return sample
[docs] def contains(self, x: Any) -> bool:
"""Return boolean specifying if x is a valid member of this space."""
if not isinstance(x, np.ndarray):
gym.logger.warn("Casting input x to numpy array.")
try:
x = np.asarray(x, dtype=self.dtype)
except (ValueError, TypeError):
return False
return bool(
np.can_cast(x.dtype, self.dtype)
and x.shape == self.shape
and np.all(x >= self.low)
and np.all(x <= self.high)
)
[docs] def to_jsonable(self, sample_n: Sequence[NDArray[Any]]) -> list[list]:
"""Convert a batch of samples from this space to a JSONable data type."""
return [sample.tolist() for sample in sample_n]
[docs] def from_jsonable(self, sample_n: Sequence[float | int]) -> list[NDArray[Any]]:
"""Convert a JSONable data type to a batch of samples from this space."""
return [np.asarray(sample, dtype=self.dtype) for sample in sample_n]
def __repr__(self) -> str:
"""A string representation of this space.
The representation will include bounds, shape and dtype.
If a bound is uniform, only the corresponding scalar will be given to avoid redundant and ugly strings.
Returns:
A representation of the space
"""
return f"Box({self.low_repr}, {self.high_repr}, {self.shape}, {self.dtype})"
def __eq__(self, other: Any) -> bool:
"""Check whether `other` is equivalent to this instance. Doesn't check dtype equivalence."""
return (
isinstance(other, Box)
and (self.shape == other.shape)
and (self.dtype == other.dtype)
and np.allclose(self.low, other.low)
and np.allclose(self.high, other.high)
)
def __setstate__(self, state: Iterable[tuple[str, Any]] | Mapping[str, Any]):
"""Sets the state of the box for unpickling a box with legacy support."""
super().__setstate__(state)
# legacy support through re-adding "low_repr" and "high_repr" if missing from pickled state
if not hasattr(self, "low_repr"):
self.low_repr = array_short_repr(self.low)
if not hasattr(self, "high_repr"):
self.high_repr = array_short_repr(self.high)