Discrete-time LQG mean field games with unreliable communication

Jun Moon, Tamer Basar

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

Abstract

In this paper, we consider discrete-time linear-quadratic-Gaussian (LQG) mean field games over unreliable communication links. These are dynamic games with a large number of agents where the cost function of each agent is coupled with other agents' states via a mean field term. Further, the individual dynamical system for each agent is subject to packet dropping. Under this setup, we first obtain an optimal decentralized control law for each agent that is a function of local information as well as packet drop information. We then construct a mean field system that provides the best approximation to the mean field term under appropriate conditions. We also show that the optimal decentralized controller stabilizes the individual dynamical system in the time-average sense. We prove an ε-Nash equilibrium property of the set of N optimal decentralized controllers, and show that ε can be made arbitrarily small as the number of agents becomes arbitrarily large. We note that the existence of the ε-Nash equilibrium obtained in this paper is primarily dependent on the underlying communication networks.

Original languageEnglish (US)
Title of host publication53rd IEEE Conference on Decision and Control,CDC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2697-2702
Number of pages6
EditionFebruary
ISBN (Electronic)9781479977468
DOIs
StatePublished - 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

Publication series

NameProceedings of the IEEE Conference on Decision and Control
NumberFebruary
Volume2015-February
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014
Country/TerritoryUnited States
CityLos Angeles
Period12/15/1412/17/14

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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