@COMMENT This file was generated by bib2html.pl version 0.90
@COMMENT written by Patrick Riley
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings(AAAI07-taylor,
author="Matthew E.\ Taylor and Shimon Whiteson and Peter Stone",
title="Temporal Difference and Policy Search Methods for
Reinforcement Learning: An Empirical Comparison",
note = "Nectar Track",
pages="1675--1678",
booktitle="Proceedings of the Twenty-Second
Conference on Artificial Intelligence",
month="July",year="2007",
abstract="Reinforcement learning (RL) methods have become
popular in recent years because of their ability to solve
complex tasks with minimal feedback. Both genetic algorithms
(GAs) and temporal difference (TD) methods have proven
effective at solving difficult RL problems, but few rigorous
comparisons have been conducted. Thus, no general guidelines
describing the methods' relative strengths and weaknesses are
available. This paper summarizes a detailed empirical
comparison between a GA and a TD method in Keepaway, a
standard RL benchmark domain based on robot soccer. The
results from this study help isolate the factors critical to
the performance of each learning method and yield insights
into their general strengths and weaknesses.",
wwwnote={AAAI
2007},
)