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https://github.com/Farama-Foundation/Gymnasium.git
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07645bd11e
* spaces equality fixes and tests * squash-merged master * added better equality tests and more checks against bad space creation
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
import numpy as np
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import gym
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from gym import logger
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from .space import Space
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class Box(Space):
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"""
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A box in R^n.
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I.e., each coordinate is bounded.
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Example usage:
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self.action_space = spaces.Box(low=-10, high=10, shape=(1,))
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"""
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def __init__(self, low=None, high=None, shape=None, dtype=None):
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"""
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Two kinds of valid input:
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Box(low=-1.0, high=1.0, shape=(3,4)) # low and high are scalars, and shape is provided
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Box(low=np.array([-1.0,-2.0]), high=np.array([2.0,4.0])) # low and high are arrays of the same shape
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"""
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if shape is None:
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assert low.shape == high.shape
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shape = low.shape
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else:
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assert np.isscalar(low) and np.isscalar(high)
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low = low + np.zeros(shape)
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high = high + np.zeros(shape)
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if dtype is None: # Autodetect type
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if (high == 255).all():
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dtype = np.uint8
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else:
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dtype = np.float32
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logger.warn("gym.spaces.Box autodetected dtype as {}. Please provide explicit dtype.".format(dtype))
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self.low = low.astype(dtype)
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self.high = high.astype(dtype)
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super(Box, self).__init__(shape, dtype)
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self.np_random = np.random.RandomState()
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def seed(self, seed):
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self.np_random.seed(seed)
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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.low.shape).astype(self.dtype)
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def contains(self, x):
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return x.shape == self.shape and (x >= self.low).all() and (x <= self.high).all()
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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 np.allclose(self.low, other.low) and np.allclose(self.high, other.high)
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