Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/731
Title: On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam's Razor
Authors: TUYLS, Karl 
Verbeeck, Katja
Maes, Sam
Issue Date: 2003
Publisher: Springer
Source: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, 2691. p. 335-344
Abstract: Modeling learning agents in the context of Multi-agent Systems requires an adequate understanding of their dynamic behaviour. Usually, these agents are modeled similar to the different players in a standard game theoretical model. Unfortunately traditional Game Theory is static and limited in its usefelness. Evolutionary Game Theory improves on this by providing a dynamics which describes how strategies evolve over time. In this paper, we discuss three learning models whose dynamics are related to the Replicator Dynamics(RD). We show how a classical Reinforcement Learning(RL) technique, i.e. Q-learning relates to the RD. This allows to better understand the learning process and it allows to determine how complex a RL model should be. More precisely, Occam's Razor applies in the framework of games, i.e. the simplest model (Cross) suffices for learning equilibria. An experimental verification in all three models is presented.
Document URI: http://hdl.handle.net/1942/731
Link to publication: http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2691&spage=335
ISSN: 0302-9743
ISI #: 000184854600032
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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