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Linear-Quadratic Graphon Mean Field Games with Common Noise

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arxiv 2401.09030 v4 pith:TQME2WKQ submitted 2024-01-17 math.OC

classification math.OC
keywords commongraphonnoisefieldgameslimitlq-gmfgsmean
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abstract

This paper studies linear quadratic graphon mean field games (LQ-GMFGs) with common noise, in which a large number of agents are coupled via a weighted undirected graph. One special feature, compared with the well-studied graphon mean field games, is that the states of agents are described by the dynamic systems with the idiosyncratic noises and common noise. The limit LQ-GMFGs with common noise are formulated based on the assumption that these graphs lie in a sequence converging to a limit graphon. By applying the spectral decomposition method, the existence of solution for the formulated limit LQ-GMFGs is derived. Moreover, based on the adequate convergence assumptions, a set of $\epsilon$-Nash equilibrium strategies for the finite large population problem is constructed.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games

    math.OC 2025-06 accept novelty 7.0 of 10

    A bilevel policy optimization algorithm for continuous-time linear-quadratic graphon mean field games converges linearly to best-response policies and globally to the Nash equilibrium.

  2. Linear-quadratic stochastic nonzero-sum differential games between graphon teams

    math.OC 2025-06 conditional novelty 6.0 of 10

    For a linear-quadratic nonzero-sum game between two graphon teams, the paper derives a Nash equilibrium from coupled Riccati equations and proves existence for sufficiently small cross-team coupling.

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