Source code for stable_baselines.common.noise

import numpy as np

[docs]class AdaptiveParamNoiseSpec(object): """ Implements adaptive parameter noise :param initial_stddev: (float) the initial value for the standard deviation of the noise :param desired_action_stddev: (float) the desired value for the standard deviation of the noise :param adoption_coefficient: (float) the update coefficient for the standard deviation of the noise """ def __init__(self, initial_stddev=0.1, desired_action_stddev=0.1, adoption_coefficient=1.01): self.initial_stddev = initial_stddev self.desired_action_stddev = desired_action_stddev self.adoption_coefficient = adoption_coefficient self.current_stddev = initial_stddev
[docs] def adapt(self, distance): """ update the standard deviation for the parameter noise :param distance: (float) the noise distance applied to the parameters """ if distance > self.desired_action_stddev: # Decrease stddev. self.current_stddev /= self.adoption_coefficient else: # Increase stddev. self.current_stddev *= self.adoption_coefficient
[docs] def get_stats(self): """ return the standard deviation for the parameter noise :return: (dict) the stats of the noise """ return {'param_noise_stddev': self.current_stddev}
def __repr__(self): fmt = 'AdaptiveParamNoiseSpec(initial_stddev={}, desired_action_stddev={}, adoption_coefficient={})' return fmt.format(self.initial_stddev, self.desired_action_stddev, self.adoption_coefficient)
class ActionNoise(object): """ The action noise base class """ def reset(self): """ call end of episode reset for the noise """ pass
[docs]class NormalActionNoise(ActionNoise): """ A Gaussian action noise :param mean: (float) the mean value of the noise :param sigma: (float) the scale of the noise (std here) """ def __init__(self, mean, sigma): self._mu = mean self._sigma = sigma def __call__(self): return np.random.normal(self._mu, self._sigma) def __repr__(self): return 'NormalActionNoise(mu={}, sigma={})'.format(self._mu, self._sigma)
[docs]class OrnsteinUhlenbeckActionNoise(ActionNoise): """ A Ornstein Uhlenbeck action noise, this is designed to approximate brownian motion with friction. Based on :param mean: (float) the mean of the noise :param sigma: (float) the scale of the noise :param theta: (float) the rate of mean reversion :param dt: (float) the timestep for the noise :param initial_noise: ([float]) the initial value for the noise output, (if None: 0) """ def __init__(self, mean, sigma, theta=.15, dt=1e-2, initial_noise=None): self._theta = theta self._mu = mean self._sigma = sigma self._dt = dt self.initial_noise = initial_noise self.noise_prev = None self.reset() def __call__(self): noise = self.noise_prev + self._theta * (self._mu - self.noise_prev) * self._dt + \ self._sigma * np.sqrt(self._dt) * np.random.normal(size=self._mu.shape) self.noise_prev = noise return noise
[docs] def reset(self): """ reset the Ornstein Uhlenbeck noise, to the initial position """ self.noise_prev = self.initial_noise if self.initial_noise is not None else np.zeros_like(self._mu)
def __repr__(self): return 'OrnsteinUhlenbeckActionNoise(mu={}, sigma={})'.format(self._mu, self._sigma)