Source code for

"""A Space representing a rectangular region of space."""
import warnings

import numpy as np

from import Space

[docs]class Box(Space): """A box in R^n. Each coordinate is bounded above and below. """ def __init__(self, low, high, shape=None, dtype=np.float32): """ Initialize the Box. Two kinds of bounds are supported, scalars and arrays: Box(-1.0, 1.0, (3,4)) # low and high are scalars, and shape is provided Box(np.array([-1.0,-2.0]), np.array([2.0,4.0])) # low and high are arrays of the same shape If dtype is not specified, we assume dtype to be np.float32, but when low=0 and high=255, it is very likely to be np.uint8. We warn user. It is different from gym.Box, where they warn user as long as dtype is not specified. """ if shape is None: assert low.shape == high.shape self.low = low self.high = high else: assert np.isscalar(low) and np.isscalar(high) self.low = low + np.zeros(shape) self.high = high + np.zeros(shape) if (self.low == 0).all() and ( self.high == 255).all() and dtype != np.uint8: warnings.warn("Creating a akro.Box with low=0, high=255 and " "dtype=np.float32.") self._dtype = dtype
[docs] def sample(self): """Uniformly randomly sample a random element of this space.""" if self.dtype == np.uint8: # since np.random.randint() does not accept array as input low = np.take(self.low, 0) high = np.take(self.high, 0) return np.random.randint( low=low, high=high + 1, size=self.low.shape).astype( self.dtype, copy=False) else: return np.random.uniform( low=self.low, high=self.high, size=self.low.shape).astype( self.dtype, copy=False)
[docs] def contains(self, x): """Return boolean specifying if x is a valid member of this space.""" return x.shape == self.shape and (x >= self.low).all() and ( x <= self.high).all()
@property def dtype(self): """Return the dtype of samples contained in this Space.""" return self._dtype @property def shape(self): """Return the shape of samples contained in this Space.""" return self.low.shape @property def flat_dim(self): """Return the length of the flattened vector of the space.""" return @property def bounds(self): """Return a 2-tuple containing the lower and upper bounds.""" return self.low, self.high
[docs] def flatten(self, x): """ Return a flattened observation x. Returns: x (flattened) """ return np.asarray(x).flatten()
[docs] def unflatten(self, x): """ Return an unflattened observation x. Returns: x (unflattened) """ return np.asarray(x).reshape(self.shape)
[docs] def flatten_n(self, xs): """ Return flattened observations xs. Returns: xs (flattened) """ xs = np.asarray(xs) return xs.reshape((xs.shape[0], -1))
[docs] def unflatten_n(self, xs): """ Return unflattened observations xs. Returns: xs (unflattened) """ xs = np.asarray(xs) return xs.reshape((xs.shape[0], ) + self.shape)
def __repr__(self): """Compute a representation of the Box Space.""" return "Box" + str(self.shape) def __eq__(self, other): """Compare two Box Spaces for approximate equality.""" return isinstance(other, Box) \ and np.allclose(self.low, other.low) \ and np.allclose(self.high, other.high) def __hash__(self): """Hash the Box Space by value.""" return hash((self.low, self.high))
[docs] def new_tensor_variable(self, name, extra_dims): """ Create a tensor variable given the name and extra dimensions. :param name: name of the variable :param extra_dims: extra dimensions in the front :return: the created tensor variable """ raise NotImplementedError