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60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
import numpy as np
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from .space import Space
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class Box(Space):
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"""A box in R^n, i.e.each coordinate is bounded.
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There are two common use cases:
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* Identical bound for each dimension::
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>>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)
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Box(3, 4)
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* Independent bound for each dimension::
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>>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)
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Box(2,)
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"""
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def __init__(self, low, high, shape=None, dtype=np.float32):
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assert dtype is not None, 'dtype must be explicitly provided. '
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self.dtype = np.dtype(dtype)
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if shape is None:
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assert low.shape == high.shape
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self.shape = low.shape
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self.low = low
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self.high = high
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else:
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assert np.isscalar(low) and np.isscalar(high)
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self.shape = tuple(shape)
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self.low = np.full(self.shape, low)
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self.high = np.full(self.shape, high)
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low = low + np.zeros(shape)
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high = high + np.zeros(shape)
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self.low = self.low.astype(self.dtype)
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self.high = self.high.astype(self.dtype)
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super(Box, self).__init__(self.shape, self.dtype)
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def sample(self):
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high = self.high if self.dtype.kind == 'f' else self.high.astype('int64') + 1
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return self.np_random.uniform(low=self.low, high=high, size=self.shape).astype(self.dtype)
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def contains(self, x):
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if isinstance(x, list):
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x = np.array(x) # Promote list to array for contains check
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return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)
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def to_jsonable(self, sample_n):
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return np.array(sample_n).tolist()
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def from_jsonable(self, sample_n):
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return [np.asarray(sample) for sample in sample_n]
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def __repr__(self):
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return "Box" + str(self.shape)
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def __eq__(self, other):
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return isinstance(other, Box) and (self.shape == other.shape) and np.allclose(self.low, other.low) and np.allclose(self.high, other.high)
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