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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.
@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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