Qualche esempio di codice di algoritmo REINFORCE proposto da Williams?

Question

Se uno sa qualsiasi codice di esempio di un algoritmo Ronald J. Williams proposto in
A class of gradient-estimating algorithms for reinforcement learning in neural networks

Answer 1

Sì, fare una ricerca su GitHub, e si otterrà un sacco di risultati:

GitHub: WILLIAMS+REINFORCE

Il i più popolari usano questo codice (in Python):

__author__ = 'Thomas Rueckstiess, ruecksti@in.tum.de' 

from pybrain.rl.learners.directsearch.policygradient import PolicyGradientLearner 
from scipy import mean, ravel, array 


class Reinforce(PolicyGradientLearner): 
""" Reinforce is a gradient estimator technique by Williams (see 
    "Simple Statistical Gradient-Following Algorithms for 
    Connectionist Reinforcement Learning"). It uses optimal 
    baselines and calculates the gradient with the log likelihoods 
    of the taken actions. """ 

def calculateGradient(self): 
    # normalize rewards 
    # self.ds.data['reward'] /= max(ravel(abs(self.ds.data['reward']))) 

    # initialize variables 
    returns = self.dataset.getSumOverSequences('reward') 
    seqidx = ravel(self.dataset['sequence_index']) 

    # sum of sequences up to n-1 
    loglhs = [sum(self.loglh['loglh'][seqidx[n]:seqidx[n + 1], :]) for n in range(self.dataset.getNumSequences() - 1)] 
    # append sum of last sequence as well 
    loglhs.append(sum(self.loglh['loglh'][seqidx[-1]:, :])) 
    loglhs = array(loglhs) 

    baselines = mean(loglhs ** 2 * returns, 0)/mean(loglhs ** 2, 0) 
    # TODO: why gradient negative? 
    gradient = -mean(loglhs * (returns - baselines), 0) 

    return gradient