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dc.contributor.authorIzquierdo Millán, Luis Rodrigo 
dc.contributor.authorIzquierdo, Segismundo S.
dc.contributor.authorRodríguez, Javier
dc.date.accessioned2022-07-19T12:14:52Z
dc.date.available2022-07-19T12:14:52Z
dc.date.issued2022-06
dc.identifier.issn2325-5870
dc.identifier.urihttp://hdl.handle.net/10259/6776
dc.description.abstractOver the past few years, the scientific community has been studying the usefulness of evolutionary game theory to solve distributed control problems. In this paper we analyze a simple version of the Best Experienced Payoff (BEP) algorithm, a revision protocol recently proposed in the evolutionary game theory literature. This revision protocol is simple, completely decentralized and has minimum information requirements. Here we prove that adding some noise to this protocol can lead to efficient results in single-optimum coordination problems in little time, even in large populations of agents. We also test the algorithm under a wide range of different conditions using computer simulation. In particular, we consider different numbers of agents and of strategies, and we analyze the robustness of the algorithm to different updating schemes (e.g. synchronous vs asynchronous) and to different types of interaction networks (e.g. ring, preferential attachment, small world and complete). In all cases, using the noisy version of BEP, the agents quickly approach a small neighborhood of the optimal state from every initial condition, and spend most of the time in that neighborhood.en
dc.description.sponsorshipFinancial support from the Spanish State Research Agency (PID2020-118906GBI00/AEI/10.13039/501100011033), from the Regional Government of Castilla y Leon and the EU-FEDER program (CLU-2019-04), from the ´ Spanish Ministry of Science, Innovation and Universities, and from the Fulbright Program (PRX19/00113, PRX21/00295), is gratefully acknowledged.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofIEEE Transactions on Control of Network Systems. 2022en
dc.subjectBest experienced payoffen
dc.subjectDecentralized algorithmsen
dc.subjectDistributed controlen
dc.subjectEvolutionary dynamicsen
dc.subjectEvolutionary game theoryen
dc.subjectLarge population double limiten
dc.subjectSmall noise limiten
dc.subject.otherIngenieríaes
dc.subject.otherEngineeringen
dc.titleFast and Scalable Global Convergence in Single-Optimum Decentralized Coordination Problemsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccesses
dc.relation.publisherversionhttps://doi.org/10.1109/TCNS.2022.3181545es
dc.identifier.doi10.1109/TCNS.2022.3181545
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118906GB-I00/ES/INTERACCIONES DINAMICAS DISTRIBUIDAS: PROTOCOLOS BEST EXPERIENCED PAYOFF Y SEPARACION ENDOGENAes
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Castilla y León//CLU-2019-04es
dc.relation.projectIDinfo:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PRX19%2F00113en
dc.relation.projectIDinfo:eu-repo/grantAgreement/MIU//PRX21%2F00295en
dc.identifier.essn2325-5870
dc.identifier.essn2372-2533
dc.journal.titleIEEE Transactions on Control of Network Systemsen
dc.page.initial1es
dc.page.final12es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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