Mean field LQG games with model uncertainty

Jianhui Huang, Minyi Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)

Abstract

This paper considers a class of mean field linearquadratic- Gaussian (MFLQG) games. The system consists of a large number of negligible agents coupled through their cost functionals. Different to previous mean field game modeling, the stochastic differential equations of agents in our setup are subject to deterministic drift uncertainty satisfying an integral quadratic constraint. We deal with the model uncertainty by a robust optimization approach and formulate a minimax control problem in the infinite population limit. The state aggregation technique is applied where the mean field changes with the drift uncertainty which acts as an adversarial player. Based on the variational and Lagrange multiplier methods, a set of decentralized strategies is derived. The associated Hamiltonian system is represented by a system of coupled mean-field forward backward stochastic differential equations (FBSDEs) and some decoupling methods by Riccati equations are also presented.
Original languageEnglish
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherIEEE
Pages3103-3108
Number of pages6
ISBN (Print)9781467357173
DOIs
Publication statusPublished - 1 Jan 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: 10 Dec 201313 Dec 2013

Conference

Conference52nd IEEE Conference on Decision and Control, CDC 2013
Country/TerritoryItaly
CityFlorence
Period10/12/1313/12/13

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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