2024-03-28T23:31:22Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/67762022-11-02T13:35:52Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832
Fast and Scalable Global Convergence in Single-Optimum Decentralized Coordination Problems
Izquierdo Millán, Luis Rodrigo
Izquierdo, Segismundo S.
Rodríguez, Javier
Best experienced payoff
Decentralized algorithms
Distributed control
Evolutionary dynamics
Evolutionary game theory
Large population double limit
Small noise limit
Over 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.
2022-07-19T12:14:52Z
2022-07-19T12:14:52Z
2022-07-19T12:14:52Z
2022-06
info:eu-repo/semantics/article
2325-5870
http://hdl.handle.net/10259/6776
10.1109/TCNS.2022.3181545
2325-5870
2372-2533
eng
IEEE Transactions on Control of Network Systems. 2022
https://doi.org/10.1109/TCNS.2022.3181545
info: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 ENDOGENA
info:eu-repo/grantAgreement/Junta de Castilla y León//CLU-2019-04
info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PRX19%2F00113
info:eu-repo/grantAgreement/MIU//PRX21%2F00295
info:eu-repo/semantics/embargoedAccess
Institute of Electrical and Electronics Engineers (IEEE)