Kagan Tumer's Publications

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Graphical Models in Continuous Domains for Multiagent Reinforcement Learning (extended abstract). S. Proper and K. Tumer. In Proceedings of the Twelveth International Joint Conference on Autonomous Agents and Multiagent Systems, Minneapolis, MN, May 2013.

Abstract

In this paper 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 continuous state and action spaces.

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BibTeX Entry

@inproceedings{tumer-proper_aamas13,
        author = {S. Proper and  K. Tumer},
        title = {Graphical Models in Continuous Domains for Multiagent Reinforcement Learning (extended abstract)},
        booktitle = {Proceedings of the Twelveth International Joint Conference on Autonomous Agents and Multiagent Systems},
	month = {May},
	address = {Minneapolis, MN},
	abstract={In this paper 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 continuous state and action spaces.},
	bib2html_pubtype = {Refereed Conference Papers},
	bib2html_rescat = {Multiagent Systems, Reinforcement Learning},
        year = {2013}
}

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