44 lines
2.3 KiB
Python
44 lines
2.3 KiB
Python
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# An old version of OpenAI Gym's multi_discrete.py. (Was getting affected by Gym updates)
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# (https://github.com/openai/gym/blob/1fb81d4e3fb780ccf77fec731287ba07da35eb84/gym/spaces/multi_discrete.py)
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import numpy as np
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import gym
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from gym.spaces import prng
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class MultiDiscrete(gym.Space):
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"""
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- The multi-discrete action space consists of a series of discrete action spaces with different parameters
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- It can be adapted to both a Discrete action space or a continuous (Box) action space
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- It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space
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- It is parametrized by passing an array of arrays containing [min, max] for each discrete action space
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where the discrete action space can take any integers from `min` to `max` (both inclusive)
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Note: A value of 0 always need to represent the NOOP action.
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e.g. Nintendo Game Controller
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- Can be conceptualized as 3 discrete action spaces:
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1) Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4
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2) Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
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3) Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
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- Can be initialized as
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MultiDiscrete([ [0,4], [0,1], [0,1] ])
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"""
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def __init__(self, array_of_param_array):
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self.low = np.array([x[0] for x in array_of_param_array])
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self.high = np.array([x[1] for x in array_of_param_array])
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self.num_discrete_space = self.low.shape[0]
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def sample(self):
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""" Returns a array with one sample from each discrete action space """
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# For each row: round(random .* (max - min) + min, 0)
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random_array = prng.np_random.rand(self.num_discrete_space)
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return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)]
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def contains(self, x):
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return len(x) == self.num_discrete_space and (np.array(x) >= self.low).all() and (np.array(x) <= self.high).all()
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@property
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def shape(self):
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return self.num_discrete_space
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def __repr__(self):
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return "MultiDiscrete" + str(self.num_discrete_space)
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def __eq__(self, other):
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return np.array_equal(self.low, other.low) and np.array_equal(self.high, other.high)
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