• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
Online Kernel Selection for Bayesian Reinforcement Learning.
Joseph
Reisinger, Peter Stone, and Risto
Miikkulainen.
In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008.
ICML 2008
[PDF]434.0kB [postscript]1.7MB
Kernel-based Bayesian methods for Reinforcement Learning (RL) such as Gaussian Process Temporal Difference (GPTD) are particularly promising because they rigorously treat uncertainty in the value function and make it easy to specify prior knowledge. However, the choice of prior distribution significantly affects the empirical performance of the learning agent, and little work has been done extending existing methods for prior model selection to the online setting. This paper develops Replacing-Kernel RL, an online model selection method for GPTD using sequential Monte-Carlo methods. Replacing-Kernel RL is compared to standard GPTD and tile-coding on several RL domains, and is shown to yield significantly better asymptotic performance for many different kernel families. Furthermore, the resulting kernels capture an intuitively useful notion of prior state covariance that may nevertheless be difficult to capture manually.
@InProceedings{ICML08-reisinger,
author="Joseph Reisinger and Peter Stone and Risto Miikkulainen",
title="Online Kernel Selection for Bayesian Reinforcement Learning",
booktitle="Proceedings of the Twenty-Fifth International Conference on Machine Learning",
month="July",year="2008",
abstract={ Kernel-based Bayesian methods for Reinforcement Learning
(RL) such as Gaussian Process Temporal Difference (GPTD) are
particularly promising because they rigorously treat
uncertainty in the value function and make it easy to specify
prior knowledge. However, the choice of prior distribution
significantly affects the empirical performance of the learning
agent, and little work has been done extending existing methods
for prior model selection to the online setting. This paper
develops Replacing-Kernel RL, an online model selection method
for GPTD using sequential Monte-Carlo methods. Replacing-Kernel
RL is compared to standard GPTD and tile-coding on several RL
domains, and is shown to yield significantly better asymptotic
performance for many different kernel families. Furthermore, the
resulting kernels capture an intuitively useful notion of prior
state covariance that may nevertheless be difficult to capture
manually. },
wwwnote={<a href="http://icml2008.cs.helsinki.fi/">ICML 2008</a>},
}
Generated by bib2html.pl (written by Patrick Riley ) on Wed Apr 17, 2024 18:42:55