Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/733
Title: Reinforcement Learning in Large State Spaces Simulated Robotic Soccer as a Testbed
Authors: TUYLS, Karl 
Maes, Sam
Manderick, Bernard
Issue Date: 2003
Publisher: Springer
Source: ROBOCUP 2002: ROBOT SOCCER WORLD CUP VI. p. 319-326
Series/Report: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Series/Report no.: 2752
Abstract: Large state spaces and incomplete information are two problems that stand out in learning in multi-agent systems. In this paper we tackle them both by using a combination of decision trees and Bayesian networks (BNs) to model the environment and the Q-function. Simulated robotic soccer is used as a testbed, since there agents are faced with both large state spaces and incomplete information. The long-term goal of this research is to define generic techniques that allow agents to learn in large-scaled multi-agent systems.
Document URI: http://hdl.handle.net/1942/733
ISBN: 3-540-40666-2
ISSN: 0302-9743
DOI: 10.1007/b11927
ISI #: 000185884000027
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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