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Computer Science > Machine Learning

arXiv:2501.17450 (cs)
[Submitted on 29 Jan 2025]

Title:NF-MKV Net: A Constraint-Preserving Neural Network Approach to Solving Mean-Field Games Equilibrium

Authors:Jinwei Liu, Lu Ren, Wang Yao, Xiao Zhang
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Abstract:Neural network-based methods for solving Mean-Field Games (MFGs) equilibria have garnered significant attention for their effectiveness in high-dimensional problems. However, many algorithms struggle with ensuring that the evolution of the density distribution adheres to the required mathematical constraints. This paper investigates a neural network approach to solving MFGs equilibria through a stochastic process perspective. It integrates process-regularized Normalizing Flow (NF) frameworks with state-policy-connected time-series neural networks to address McKean-Vlasov-type Forward-Backward Stochastic Differential Equation (MKV FBSDE) fixed-point problems, equivalent to MFGs equilibria.
Comments: 7 pages
Subjects: Machine Learning (cs.LG)
MSC classes: 68T07
ACM classes: I.2.6
Cite as: arXiv:2501.17450 [cs.LG]
  (or arXiv:2501.17450v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2501.17450

Submission history

From: Jinwei Liu [view email]
[v1] Wed, 29 Jan 2025 07:14:09 UTC (5,088 KB)
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