import numpy as np
from mushroom_rl.algorithms.policy_search.black_box_optimization import BlackBoxOptimization
from mushroom_rl.utils.parameters import to_parameter
class RWR(BlackBoxOptimization):
"""
Reward-Weighted Regression algorithm.
"A Survey on Policy Search for Robotics", Deisenroth M. P., Neumann G.,
Peters J.. 2013.
"""
def __init__(self, mdp_info, distribution, policy, beta, features=None):
"""
Constructor.
Args:
beta ([float, Parameter]): the temperature for the exponential reward
transformation.
"""
self._beta = to_parameter(beta)
self._add_save_attr(_beta='mushroom')
super().__init__(mdp_info, distribution, policy, features)
def _update(self, Jep, theta):
Jep -= np.max(Jep)
d = np.exp(self._beta() * Jep)
self.distribution.mle(theta, d)