Finite mean field games: fictitious play and convergence to a first order continuous mean field game

DOI10.1016/j.matpur.2019.02.006zbMath1427.35288arXiv1805.05940OpenAlexW2963028505WikidataQ128421516 ScholiaQ128421516MaRDI QIDQ2338129

Francisco J. Silva, Saeed Hadikhanloo

Publication date: 21 November 2019

Published in: Journal de Mathématiques Pures et Appliquées. Neuvième Série (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1805.05940

zbMATH Keywords

fictitious playmean field gamesapproximation of first order mean field gamesfinite time and finite state space

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs (65M12) Rationality and learning in game theory (91A26) PDEs in connection with game theory, economics, social and behavioral sciences (35Q91) Mean field games (aspects of game theory) (91A16)

Related Items (16)

Random features for high-dimensional nonlocal mean-field games ⋮ A probabilistic numerical method for a class of mean field games ⋮ Linear programming fictitious play algorithm for mean field games with optimal stopping and absorption ⋮ Error estimates of a theta-scheme for second-order mean field games ⋮ Discrete potential mean field games: duality and numerical resolution ⋮ Generalized conditional gradient and learning in potential mean field games ⋮ High order computation of optimal transport, mean field planning, and potential mean field games ⋮ Graphon mean-field control for cooperative multi-agent reinforcement learning ⋮ Learning Optimal Policies in Potential Mean Field Games: Smoothed Policy Iteration Algorithms ⋮ Machine learning architectures for price formation models ⋮ A Lagrange–Galerkin Scheme for First Order Mean Field Game Systems ⋮ Value iteration algorithm for mean-field games ⋮ An introduction to mean field game theory ⋮ Computational Methods for First-Order Nonlocal Mean Field Games with Applications ⋮ Splitting methods for a class of non-potential mean field games ⋮ Multi-agent reinforcement learning: a selective overview of theories and algorithms

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