import numpy as np
[docs]class Box:
"""
This class implements functions to manage continuous states and action
spaces. It is similar to the ``Box`` class in ``gym.spaces.box``.
"""
[docs] def __init__(self, low, high, shape=None):
"""
Constructor.
Args:
low ([float, np.ndarray]): the minimum value of each dimension of
the space. If a scalar value is provided, this value is
considered as the minimum one for each dimension. If a
np.ndarray is provided, each i-th element is considered the
minimum value of the i-th dimension;
high ([float, np.ndarray]): the maximum value of dimensions of the
space. If a scalar value is provided, this value is considered
as the maximum one for each dimension. If a np.ndarray is
provided, each i-th element is considered the maximum value
of the i-th dimension;
shape (np.ndarray, None): the dimension of the space. Must match
the shape of ``low`` and ``high``, if they are np.ndarray.
"""
if shape is None:
self._low = low
self._high = high
self._shape = low.shape
else:
self._low = low
self._high = high
self._shape = shape
if np.isscalar(low) and np.isscalar(high):
self._low += np.zeros(shape)
self._high += np.zeros(shape)
assert self._low.shape == self._high.shape
@property
def low(self):
"""
Returns:
The minimum value of each dimension of the space.
"""
return self._low
@property
def high(self):
"""
Returns:
The maximum value of each dimension of the space.
"""
return self._high
@property
def shape(self):
"""
Returns:
The dimensions of the space.
"""
return self._shape
[docs]class Discrete:
"""
This class implements functions to manage discrete states and action
spaces. It is similar to the ``Discrete`` class in ``gym.spaces.discrete``.
"""
[docs] def __init__(self, n):
"""
Constructor.
Args:
n (int): the number of values of the space.
"""
self.values = np.arange(n)
self.n = n
@property
def size(self):
"""
Returns:
The number of elements of the space.
"""
return self.n,
@property
def shape(self):
"""
Returns:
The shape of the space that is always (1,).
"""
return 1,