REVIEW 2 cited by
Linear-Quadratic Graphon Mean Field Games with Common Noise
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
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.
Forward citations
Cited by 2 Pith papers
-
Policy Optimization for Continuous-time Linear-Quadratic Graphon Mean Field Games
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.
-
Linear-quadratic stochastic nonzero-sum differential games between graphon teams
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.
Discussion (0). Continue with ORCID to comment.