Mean Field Games

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Overview

Mean field games study how an individual’s best decision depends on the evolving distribution of many other agents, and how those decisions in turn shape that distribution. Researchers characterize equilibria with coupled Hamilton–Jacobi–Bellman and Fokker–Planck equations or McKean–Vlasov forward-backward stochastic equations, and compute them using numerical solvers, neural networks, and reinforcement learning. Work also uses these models for inverse learning, robust and risk-averse decision-making, and applications such as mobility, energy control, federated privacy, and generative modeling. A central challenge is making equilibrium computation scalable while preserving reliable guarantees under finite populations, partial information, common noise, and model uncertainty.

Latest papers

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19 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Entropy-Regularized Mean-Field Games with Average-Reward Criterion: Span-Seminorm Contractions and Model-Free Learning

    Oct 11, 2026Gunes Akbas, Naci Saldi, Berkay Anahtarci +1Mean Field GamesModel-Free RL

    Department of Mathematics, Bilkent University, Ankara 06800, Türkiye · Department of Mathematical Engineering, Özyeğin University, İstanbul 34794, Türkiye

  2. Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games

    Oct 7, 2026Bhavini Jeloka, Siddhartha Ganguly, Panagiotis TsiotrasMean Field Games

    Daniel Guggenheim School of Aerospace, Georgia Institute of Technology, Atlanta, USA

  3. Mean field games as a tool for AI safety: a worked example from the July 2026 Hugging Face incident

    Oct 1, 2026P. Jameson GraberMulti-Agent CoordinationMean Field Games

  4. Inverse Learning of the Altruism and Cost Level in Mixed-Individual Mean Field Games

    Sep 11, 2026Haoyang Cao, Gökçe Dayanıklı, Xiaofei ShiMean Field GamesGame Theory

    Department of Applied Mathematics and Statistics, Johns Hopkins University · Department of Statistics, University of Illinois, Urbana-Champaign · Department of Statistical Sciences, University of Toronto

  5. Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations

    Aug 26, 2026Aditya Makkar, Benjamin Unger, Jeongyeol Kwon +3Mean-Field MARLReinforcement Learning

    New York University · ETH Zurich · Meta Platforms Inc. +1

  6. Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View

    Jul 25, 2026Kun Zhao, Xu ChenMean Field GamesPrivacy-Preserving Federated Learning

  7. All in One: Generative Modeling as Mean-Field Game Design

    Jul 25, 2026Kun Zhao, Xu ChenMean Field GamesGenerative Modeling

  8. Neural Parameter Calibration for Finite-State Mean Field Games

    Jun 22, 2026Anna C. M. Thöni, Grégoire Lambrecht, Gökçe Dayanıklı +3Parameter EstimationMean Field Games

    Radboud University, Nijmegen, the Netherlands · New York University, New York, NY · University of Illinois Urbana-Champaign, Champaign, IL +2

  9. Stationary Robust Mean-Field Games under Model Mismatches

    Jun 21, 2026Yue WangMean Field GamesGame Theory

    Department of Electrical and Computer Engineering University of Central Florida Orlando, Florida, USA

  10. Optimal Coarse Correlated Equilibria in Mean Field Games: Linear Programming and No-Regret Learning

    Jun 18, 2026Luciano Campi, Federico Cannerozzi, Ioannis TzouanasMean Field GamesGame Theory

    DEPARTMENT OF MATHEMATICS “FEDERIGO ENRIQUES”, UNIVERSITY OF MILAN, VIA SALDINI 50, 20133, MILAN, ITALY. · CENTER FOR MATHEMATICAL ECONOMICS (IMW), BIELEFELD UNIVERSITY, BIELEFELD, GERMANY.

  11. Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games with Average Reward

    Jun 15, 2026Şevket Kaan Alkır, Naci Saldı, Berkay Anahtarcı +1Reinforcement LearningAverage-Reward RL

    Department of Mathematics Bilkent University 06800 Ankara, Turkey · Department of Mathematical Engineering Özyeğin University 34794 Istanbul, Turkey

  12. Mean-Field Diffuser: Scaling Offline MARL to Thousands of Agents

    May 28, 2026Wenhao Li, Xiangfeng Wang, Bo JinMulti-Agent System OptimizationMean Field Games

    Tongji University · East China Normal University

  13. Kernel-based potential mean-field games with unbiased random Fourier UU-statistics

    May 28, 2026Yumiharu NakanoMulti-Agent CoordinationMean Field Games

    Department of Mathematical and Computing Science, Institute of Science Tokyo

  14. MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria

    May 6, 2026Xuwei Yang, David B. Emerson, Fatemeh Tavakoli +1Nash EquilibriumFederated RL

    Department of Mathematics and Statistics McMaster University Ontario, Canada · Vector Institute Ontario, Canada

  15. Population-Aware Imitation Learning in Mean-field Games with Common Noise

    May 5, 2026Grégoire Lambrecht, Mathieu LaurièreMean Field GamesAdversarial Imitation Learning

    Center for Data Science, New York University and NYU Shanghai, New York, United States of America · Shanghai Center for Data Science; NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai; NYU Shanghai, Shanghai, People’s Republic of China.

  16. Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents

    Feb 23, 2026Michael ChertkovDiffusion ModelsMulti-Agent Control

    Graduate Interdisciplinary Program in Applied Mathematics & Department of Mathematics, University of Arizona, Tucson, AZ, USA

  17. Recurrent Structural Policy Gradient for Partially Observable Mean Field Games

    Feb 23, 2026Clarisse Wibault, Johannes Forkel, Sebastian Towers +9Mean Field GamesPolicy Gradient Methods

    FLAIR, University of Oxford · MLRG, University of Oxford · ifo Institute, LMU Munich +5

  18. Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise

    Dec 16, 2025Felipe J. P. Antunes, Yuri F. Saporito, Sebastian JaimungalStochastic Differential EquationsMean Field Games

    School of Applied Mathematics, Getulio Vargas Foundation, Brazil. · Oxford-Man Institute for Quantitative Finance, University of Oxford

  19. Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problems

    May 1, 2025Xianjin Yang, Jingguo ZhangMean Field GamesStochastic Optimal Control

    Department of Computing and Mathematical Sciences, California Institute of Technology, CA, USA. · Department of Mathematics and Risk Management Institute, National University of Singapore, Singapore.