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Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison.
Matthew
E. Taylor, Shimon Whiteson, and Peter
Stone.
In Proceedings of the Twenty-Second Conference on Artificial Intelligence, pp. 1675–1678,
July 2007. Nectar Track
AAAI 2007
[PDF]99.7kB [postscript]190.4kB
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.
@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={<a href="http://www.aaai.org/Conferences/National/2007/aaai07.html">AAAI
2007</a>},
)
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