Bayesian Policy Gradient Algorithms
Full Text: nips06.pdf
Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Conventional policy gradient methods use Monte-Carlo techniques to estimate this gradient. Since Monte Carlo methods tend to have high variance, a large number of samples is required, resulting in slow convergence. In this paper, we propose a Bayesian framework that models the policy gradient as a Gaussian process. This reduces the number of samples needed to obtain accurate gradient estimates. Moreover, estimates of the natural gradient as well as a measure of the uncertainty in the gradient estimates are provided at little extra cost.
Citation
M. Ghavamzadeh, Y. Engel. "Bayesian Policy Gradient Algorithms". Neural Information Processing Systems (NIPS), December 2006.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Ghavamzadeh+Engel:NIPS06,
author = {Mohammad Ghavamzadeh and Yaakov Engel},
title = {Bayesian Policy Gradient Algorithms},
booktitle = {Neural Information Processing Systems (NIPS)},
year = 2006,
}Last Updated: February 01, 2008Submitted by Nelson Loyola
