A Distributed Learning Algorithm with Bit-valued Communications for Multi-agent Welfare Optimization

dc.contributor.authorMenon, Anup
dc.contributor.authorBaras, John
dc.date.accessioned2013-03-11T16:56:01Z
dc.date.available2013-03-11T16:56:01Z
dc.date.issued2013
dc.description.abstractA multi-agent system comprising N agents, each picking actions from a finite set and receiving a payoff that depends on the action of the whole, is considered. The exact form of the payoffs are unknown and only their values can be measured by the respective agents. A decentralized algorithm was proposed by Marden et. al. [1] and in the authors’ earlier work [2] that, in this setting, leads to the agents picking welfare optimizing actions under some restrictive assumptions on the payoff structure. This algorithm is modified in this paper to incorporate exchange of certain bit-valued information between the agents over a directed communication graph. The notion of an interaction graph is then introduced to encode known interaction in the system. Restrictions on the payoff structure are eliminated and conditions that guarantee convergence to welfare minimizing actions w.p. 1 are derived under the assumption that the union of the interaction graph and communication graph is strongly connected.en_US
dc.description.sponsorshipResearch partially supported by the US Air Force Office of Scientific Research MURI grant FA9550-09-1-0538 and by the National Science Foundation (NSF) grant CNS-1035655.en_US
dc.identifier.urihttp://hdl.handle.net/1903/13702
dc.language.isoen_USen_US
dc.relation.isAvailableAtInstitute for Systems Researchen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.relation.ispartofseriesTR_2013-06
dc.subjectmulti-agent learningen_US
dc.subjectevolutionary gamesen_US
dc.subjectwelfare optimizationen_US
dc.subjectperturbed Markov chainsen_US
dc.titleA Distributed Learning Algorithm with Bit-valued Communications for Multi-agent Welfare Optimizationen_US
dc.typeArticleen_US

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