Kagan Tumer's Publications

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Graphical Models in Continuous Domains for Multiagent Reinforcement Learning. S. Proper and K. Tumer. In AAMAS-2012 Workshop on Adaptive and Learning Agents, Valencia, Spain, June 2012.

Abstract

Many different coordination methods have been tried with multiagent reinforcement learning in the past, but there has been very little research testing the particular domains and situations in which different coordination methods might best apply. We test two coordination methods -- difference rewards and coordination graphs -- in a continuous, multiagent rover domain using reinforcement learning, and discuss the situations in which each of these methods perform better alone or together, and why. We also contribute a novel method of applying coordination graphs in a continuous domain by taking advantage of the wire-fitting approach used to handle our continuous state and action space.

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BibTeX Entry

@incollection{tumer-proper_ala12,
        author = {S. Proper  and K. Tumer},
        title = {Graphical Models in Continuous Domains for Multiagent Reinforcement Learning},
        booktitle = {AAMAS-2012 Workshop on Adaptive and Learning Agents},
	month = {June},
	address = {Valencia, Spain},
	editors = {E. Howley and P. Vrancx and M. Knudson},
	abstract={Many different coordination methods have been tried with multiagent reinforcement learning in the past, but there has been very little research testing the particular domains and situations in which different coordination methods might best apply. We test two coordination methods -- difference rewards and coordination graphs -- in a continuous, multiagent rover domain using reinforcement learning, and discuss the situations in which each of these methods perform better alone or together, and why. We also contribute a novel method of applying coordination graphs in a continuous domain by taking advantage of the wire-fitting approach used to handle our continuous state and action space.},
	bib2html_pubtype = {Workshop/Symposium Papers},
	bib2html_rescat = {Multiagent Systems, Reinforcement Learning},
        year = {2012}
}

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