Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62217
Title: Mean-field Linear-Quadratic-Gaussian (LQG) games for stochastic integral systems
Authors: Huang, J 
Li, X 
Wang, T
Keywords: Controlled stochastic delay system
Fredholm equation
Mean field LQG games
Stochastic Volterra equation
ϵ-Nash equilibrium
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on automatic control, 2016, v. 61, no. 9, 7349148, p. 2670-2675 How to cite?
Journal: IEEE transactions on automatic control 
Abstract: In this technical note, we formulate and investigate a class of mean-field linear-quadratic-Gaussian (LQG) games for stochastic integral systems. Unlike other literature on mean-field games where the individual states follow the controlled stochastic differential equations (SDEs), the individual states in our large-population system are characterized by a class of stochastic Volterra-type integral equations. We obtain the Nash certainty equivalence (NCE) equation and hence derive the set of associated decentralized strategies. The ϵ-Nash equilibrium properties are also verified. Due to the intrinsic integral structure, the techniques and estimates applied here are significantly different from those existing results in mean-field LQG games for stochastic differential systems. For example, some Fredholm equation in the mean-field setup is introduced for the first time. As for applications, two types of stochastic delayed systems are formulated as the special cases of our stochastic integral system, and relevant mean-field LQG games are discussed.
URI: http://hdl.handle.net/10397/62217
ISSN: 0018-9286
EISSN: 1558-2523
DOI: 10.1109/TAC.2015.2506620
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