Mean field stochastic games: Convergence, Q/H-learning and optimality

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

Abstract

We consider a class of stochastic games with finite number of resource states, individual states and actions per states. At each stage, a random set of players interact. The states and the actions of all the interacting players determine together the instantaneous payoffs and the transitions to the next states. We study the convergence of the stochastic game with variable set of interacting players when the total number of possible players grow without bound. We provide sufficient conditions for mean field convergence. We characterize the mean field payoff optimality by solutions of a coupled system of backward-forward equations. The limiting games are equivalent to discrete time anonymous sequential population games or to differential population games. Using multidimensional diffusion processes, a general mean field convergence to coupled stochastic differential equation is given. Finally, the computation of mean field equilibria is addressed using Q/H learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2423-2428
Number of pages6
ISBN (Print)9781457700804
DOIs
StatePublished - 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Tembine, H. (2011). Mean field stochastic games: Convergence, Q/H-learning and optimality. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 2423-2428). [5991087] (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/acc.2011.5991087