reinforcement-learning - 威廉姆斯提出的 REINFORCE 算法的任何示例代码？

Question

有谁知道算法的任何示例代码Ronald J. Williams提出于
A class of gradient-estimating algorithms for reinforcement learning in neural networks

Answer 1

是的，在 GitHub 上搜索一下，你会得到一大堆结果:

GitHub: WILLIAMS+REINFORCE

最流行的使用此代码(在 Python 中):

__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