Nonreciprocating Sharing Methods in Cooperative Q-Learning Environments

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dc.contributor.authorCunningham, Bryanen
dc.contributor.authorCao, Yongen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:36:27Zen
dc.date.available2013-06-19T14:36:27Zen
dc.date.issued2012-06-01en
dc.description.abstractPast research on multiagent simulation with cooperative reinforcement learning (RL) focuses on developing sharing strategies that are adopted and used by all agents in the environment. In this paper, we target situations where this assumption of a single sharing strategy that is employed by all agents is not valid. We seek to address how agents with no predetermined sharing partners can exploit groups of cooperatively learning agents to improve learning performance when compared to independent learning. Specifically, we propose three intra-agent methods that do not assume a reciprocating sharing relationship and leverage the pre-existing agent interface associated with Q-Learning to expedite learning.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00001200/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001200/01/bare_conf.pdfen
dc.identifier.trnumberTR-12-15en
dc.identifier.urihttp://hdl.handle.net/10919/19430en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.relation.ispartofComputer Science Technical Reportsen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectArtificial intelligenceen
dc.titleNonreciprocating Sharing Methods in Cooperative Q-Learning Environmentsen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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