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bf783192b8
See also space.Box.contains(), it already has this change Reproduce Error with following lines: from gym import spaces import numpy as np space_Multidiscrete = spaces.MultiDiscrete(np.ones((10,3))) sample_test = np.ones((9,3)) space_Multidiscrete.contains(sample_test) #errors: ValueError: operands could not be broadcast together with shapes (9,3) (10,3)
56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
import numpy as np
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from .space import Space
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class MultiDiscrete(Space):
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"""
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- The multi-discrete action space consists of a series of discrete action spaces with different number of actions in eachs
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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 positive integers specifying number of actions for each discrete action space
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Note: Some environment wrappers assume a value of 0 always represents 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([ 5, 2, 2 ])
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"""
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def __init__(self, nvec):
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"""
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nvec: vector of counts of each categorical variable
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"""
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assert (np.array(nvec) > 0).all(), 'nvec (counts) have to be positive'
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self.nvec = np.asarray(nvec, dtype=np.int64)
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super(MultiDiscrete, self).__init__(self.nvec.shape, np.int64)
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def sample(self):
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return (self.np_random.random_sample(self.nvec.shape)*self.nvec).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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# if nvec is uint32 and space dtype is uint32, then 0 <= x < self.nvec guarantees that x
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# is within correct bounds for space dtype (even though x does not have to be unsigned)
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return x.shape == self.shape and (0 <= x).all() and (x < self.nvec).all()
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def to_jsonable(self, sample_n):
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return [sample.tolist() for sample in sample_n]
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def from_jsonable(self, sample_n):
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return np.array(sample_n)
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def __repr__(self):
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return "MultiDiscrete({})".format(self.nvec)
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def __eq__(self, other):
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return isinstance(other, MultiDiscrete) and np.all(self.nvec == other.nvec)
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