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dc.contributor.authorJaleel, Hassan
dc.contributor.authorShamma, Jeff S.
dc.date.accessioned2018-01-01T12:19:03Z
dc.date.available2018-01-01T12:19:03Z
dc.date.issued2017-10-19
dc.identifier.citationJaleel H, Shamma JS (2017) Transient Response Analysis of Metropolis Learning in Games. IFAC-PapersOnLine 50: 9661–9667. Available: http://dx.doi.org/10.1016/j.ifacol.2017.08.1927.
dc.identifier.issn2405-8963
dc.identifier.doi10.1016/j.ifacol.2017.08.1927
dc.identifier.urihttp://hdl.handle.net/10754/626610
dc.description.abstractThe objective of this work is to provide a qualitative description of the transient properties of stochastic learning dynamics like adaptive play, log-linear learning, and Metropolis learning. The solution concept used in these learning dynamics for potential games is that of stochastic stability, which is based on the stationary distribution of the reversible Markov chain representing the learning process. However, time to converge to a stochastically stable state is exponential in the inverse of noise, which limits the use of stochastic stability as an effective solution concept for these dynamics. We propose a complete solution concept that qualitatively describes the state of the system at all times. The proposed concept is prevalent in control systems literature where a solution to a linear or a non-linear system has two parts, transient response and steady state response. Stochastic stability provides the steady state response of stochastic learning rules. In this work, we study its transient properties. Starting from an initial condition, we identify the subsets of the state space called cycles that have small hitting times and long exit times. Over the long time scales, we provide a description of how the distributions over joint action profiles transition from one cycle to another till it reaches the globally optimal state.
dc.description.sponsorshipResearch supported by funding from KAUST.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S2405896317325569
dc.subjectgame theory
dc.subjectLearning theory
dc.subjectSensor networks
dc.subjectStochastic control
dc.titleTransient Response Analysis of Metropolis Learning in Games
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIFAC-PapersOnLine
kaust.personJaleel, Hassan
kaust.personShamma, Jeff S.


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