65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
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"""
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adapted from keras-rl
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"""
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from __future__ import division
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import numpy as np
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class RandomProcess(object):
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def reset_states(self):
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pass
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class AnnealedGaussianProcess(RandomProcess):
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def __init__(self, mu, sigma, sigma_min, n_steps_annealing):
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self.mu = mu
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self.sigma = sigma
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self.n_steps = 0
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if sigma_min is not None:
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self.m = -float(sigma - sigma_min) / float(n_steps_annealing)
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self.c = sigma
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self.sigma_min = sigma_min
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else:
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self.m = 0.
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self.c = sigma
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self.sigma_min = sigma
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@property
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def current_sigma(self):
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sigma = max(self.sigma_min, self.m * float(self.n_steps) + self.c)
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return sigma
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class GaussianWhiteNoiseProcess(AnnealedGaussianProcess):
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def __init__(self, mu=0., sigma=1., sigma_min=None, n_steps_annealing=1000, size=1):
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super(GaussianWhiteNoiseProcess, self).__init__(mu=mu, sigma=sigma, sigma_min=sigma_min, n_steps_annealing=n_steps_annealing)
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self.size = size
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def sample(self):
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sample = np.random.normal(self.mu, self.current_sigma, self.size)
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self.n_steps += 1
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return sample
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# Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
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class OrnsteinUhlenbeckProcess(AnnealedGaussianProcess):
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def __init__(self, theta, mu=0., sigma=1., dt=1e-2, x0=None, size=1, sigma_min=None, n_steps_annealing=1000):
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super(OrnsteinUhlenbeckProcess, self).__init__(
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mu=mu, sigma=sigma, sigma_min=sigma_min, n_steps_annealing=n_steps_annealing)
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self.theta = theta
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self.mu = mu
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self.dt = dt
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self.x0 = x0
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self.size = size
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self.reset_states()
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def sample(self):
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x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + self.current_sigma * np.sqrt(self.dt) * np.random.normal(size=self.size)
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self.x_prev = x
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self.n_steps += 1
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return x
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def reset_states(self):
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self.x_prev = self.x0 if self.x0 is not None else np.zeros(self.size)
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