Backward Mean-Field Linear-Quadratic-Gaussian (LQG) Games: Full and Partial Information

Jianhui Huang, Shujun Wang, Zhen Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)

Abstract

This paper introduces the backward mean-field (MF) linear-quadratic-Gaussian (LQG) games (for short, BMFLQG) of weakly coupled stochastic large-population system. In contrast to the well-studied forward mean-field LQG games, the individual state in our large-population system follows the backward stochastic differential equation (BSDE) whose terminal instead initial condition should be prescribed. Two classes of BMFLQG games are discussed here and their decentralized strategies are derived through the consistency condition. In the first class, the individual agents of large-population system are weakly coupled in their state dynamics and the full information can be accessible to all agents. In the second class, the coupling structure lies in the cost functional with only partial information structure. In both classes, the asymptotic near-optimality property (namely, ϵ-Nash equilibrium) of decentralized strategies are verified. To this end, some estimates to BSDE, are presented in the large-population setting.
Original languageEnglish
Article number7386584
Pages (from-to)3784-3796
Number of pages13
JournalIEEE Transactions on Automatic Control
Volume61
Issue number12
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • BSDE
  • decentralized control
  • full information
  • large-population system
  • mean-field LQG games
  • partial information
  • ϵ-Nash equilibrium

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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