OpenAI Gym - How to create one-hot observation space?

Question

Aside from openAI's doc, I hadn't been able to find a more detailed documentation.

I need to know the correct way to create:

1. An action space which has 1..n possible actions. (currently using Discrete action space)

2. An observation space that has 2^n states - A state for every possible combination of actions that has been taken. I would like a one-hot representation of the action vector - 1 for action was already taken, 0 for action still hadn't been taken

How do I do that with openAI's Gym?

Thanks

Answer 1

None of the gym.Spaces provided by the gym package at the time of writing can be used to mirror a one hot encoding representation.

Luckily for us, we can define our own space by creating a child class of gym.Spaces.

I have made such a class, which may be what you need:

import gym
import numpy as np


class OneHotEncoding(gym.Space):
    """
    {0,...,1,...,0}

    Example usage:
    self.observation_space = OneHotEncoding(size=4)
    """
    def __init__(self, size=None):
        assert isinstance(size, int) and size > 0
        self.size = size
        gym.Space.__init__(self, (), np.int64)

    def sample(self):
        one_hot_vector = np.zeros(self.size)
        one_hot_vector[np.random.randint(self.size)] = 1
        return one_hot_vector

    def contains(self, x):
        if isinstance(x, (list, tuple, np.ndarray)):
            number_of_zeros = list(x).contains(0)
            number_of_ones = list(x).contains(1)
            return (number_of_zeros == (self.size - 1)) and (number_of_ones == 1)
        else:
            return False

    def __repr__(self):
        return "OneHotEncoding(%d)" % self.size

    def __eq__(self, other):
        return self.size == other.size

You can use it thus:

-> space = OneHotEncoding(size=3)
-> space.sample()
array([0., 1., 0.])
-> space.sample()
array([1., 0., 0.])
-> space.sample()
array([0., 0., 1.])

Hope I could help