是否有与numpy中的gym.spaces.Box类似的东西？

Question

In the OpenAI gym, you can do something like this:

from gym import spaces
low = [1, 2, 3]
high = [4, 4, 4]
box = spaces.Box(np.array(low), np.array(high))

observation = np.array([2, 2, 2])
if not box.contains(observation):
    print("This is invalid!")

It basically checks for each dimension

def contains(self, obs):
    n = len(obs) # == len(low) == len(high)
    for i in range(n):
        if not (self.low[i] <= obs[i] <= self.high[i]):
            return False
    return True

Does numpy come with something like the spaces.Box class as well?

Answer 1

I do not know about a function, but it is easy to write yourself.

import numpy as np

np.all(np.less_equal(low, observation)) and np.all(np.greater_equal(observation, high))

This checks if all observations are inside the specified bounds. If you omit the np.all you can see which dimension was the problem.