Mean-field learning for satisfactory solutions

Hamidou Tembine, Raul Tempone, Pedro Vilanova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

One of the fundamental challenges in distributed interactive systems is to design efficient, accurate, and fair solutions. In such systems, a satisfactory solution is an innovative approach that aims to provide all players with a satisfactory payoff anytime and anywhere. In this paper we study fully distributed learning schemes for satisfactory solutions in games with continuous action space. Considering games where the payoff function depends only on own-action and an aggregate term, we show that the complexity of learning systems can be significantly reduced, leading to the so-called mean-field learning. We provide sufficient conditions for convergence to a satisfactory solution and we give explicit convergence time bounds. Then, several acceleration techniques are used in order to improve the convergence rate. We illustrate numerically the proposed mean-field learning schemes for quality-of-service management in communication networks. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication52nd IEEE Conference on Decision and Control
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4871-4876
Number of pages6
ISBN (Print)9781467357173
DOIs
StatePublished - Dec 2013

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