Mean Field Games

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

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Mean field games as a tool for AI safety: a worked example from the July 2026 Hugging Face incident
Oct 1, 2026P. Jameson GraberArtificial Intelligence Safety Mean Field Games
Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving
Sep 30, 2026Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang LiuMulti-Agent Reinforcement Learning Mean Field Games
Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · Daniel Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA 30332, USA
Regularized policy gradient with learned mixtures of Gaussians for games with continuous actions
Sep 29, 2026Ondřej Kubíček, Viliam Lisý, Tuomas SandholmMean Field Games Mirror Descent
Czech Technical University in Prague · Carnegie Mellon University · Artificial Intelligence Center
High-Probability Nash Regret for Decentralized Learning in Markov $α$ -Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games
Sep 14, 2026S. Rasoul EtesamiMean Field Games Observable Stochastic Game
Department of Industrial and Systems Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign, Urbana, IL, USA
Differential Games for Compositional Handling of Competing Control Tasks
Sep 1, 2026Joshua Shay KricheliObservable Stochastic Game Mean Field Games
Ben-Gurion University of the Negev, The Faculty of Natural Sciences, The Department of Computer Science
Independent Reinforcement Learning in Discounted Markov Games
Sep 1, 2026Asrin Efe Yorulmaz, Ugur Aydin, Tamer BasarMean Field Games Multi-Agent Reinforcement Learning
Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA
A Forward-Inverse Dynamic Game Framework for Enhanced Multi-Agent Trajectory Planning
Aug 3, 2026Tianle Liu, Youcheng Niu, Jing Zeng +2Mean Field Games Robust Trajectory
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
PLATO: Pointer Learner for Agent and Task Openness
Jul 27, 2026Alireza Saleh Abadi, Leen-Kiat Soh, Daniel Alan Redder +2Mean Field Games
School of Computing University of Nebraska-Lincoln · School of Computing University of Georgia · Oberlin College
All in One: Generative Modeling as Mean-Field Game Design
Jul 25, 2026Kun Zhao, Xu ChenGenerative Models Mean Field Games
Neural Parameter Calibration for Finite-State Mean Field Games
Jun 22, 2026Anna C. M. Thöni, Grégoire Lambrecht, Gökçe Dayanıklı +3Mean Field Games Mean-Field Limit
Radboud University, Nijmegen, the Netherlands · New York University, New York, NY · University of Illinois Urbana-Champaign, Champaign, IL +2
Stationary Robust Mean-Field Games under Model Mismatches
Jun 21, 2026Yue WangMean Field Games Multi-Agent Reinforcement Learning
Department of Electrical and Computer Engineering University of Central Florida Orlando, Florida, USA
Optimal Coarse Correlated Equilibria in Mean Field Games: Linear Programming and No-Regret Learning
Jun 18, 2026Luciano Campi, Federico Cannerozzi, Ioannis TzouanasMean Field Games Equilibrium
DEPARTMENT OF MATHEMATICS “FEDERIGO ENRIQUES”, UNIVERSITY OF MILAN, VIA SALDINI 50, 20133, MILAN, ITALY. · CENTER FOR MATHEMATICAL ECONOMICS (IMW), BIELEFELD UNIVERSITY, BIELEFELD, GERMANY.
Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games
Jun 17, 2026Shengyuan Ding, Xilin Wei, Xinyu Fang +4Multimodal Large Language Models Multimodal Foundation Model
1Fudan University · 2Shanghai Innovation Institute · 3Shanghai Artificial Intelligence Laboratory +2
Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games with Average Reward
Jun 15, 2026Şevket Kaan Alkır, Naci Saldı, Berkay Anahtarcı +1Mean Field Games Entropy Regularized Reinforcement Learning
Department of Mathematics Bilkent University 06800 Ankara, Turkey · Department of Mathematical Engineering Özyeğin University 34794 Istanbul, Turkey
Minimax-Optimal Policy Regret in Partially Observable Markov Games
Jun 1, 2026Raman AroraPartially Observable Markov Decision Process Mean Field Games
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Kernel-based potential mean-field games with unbiased random Fourier $U$ -statistics
May 28, 2026Yumiharu NakanoMean Field Games Maximum Mean Discrepancy
Department of Mathematical and Computing Science, Institute of Science Tokyo
Bilevel Optimization over Saddle Points of Zero-Sum Markov Games
May 26, 2026Zihao Zheng, Irwin King, Songtao LuBilevel Optimization Mean Field Games
Department of Computer Science and Engineering, The Chinese University of Hong Kong · Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong
Offline Two-Player Zero-Sum Markov Games with KL Regularization
May 13, 2026Claire Chen, Yuheng Zhang, Xinyu Liu +3Mean Field Games Observable Stochastic Game
1California Institute of Technology. · University of Illinois Urbana-Champaign. · University of Virginia. +2
Population-Aware Imitation Learning in Mean-field Games with Common Noise
May 5, 2026Grégoire Lambrecht, Mathieu LaurièreMean Field Games 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.
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
May 4, 2026Jingchu Gai, Laixi ShiMean Field Games Multi-Agent Reinforcement Learning
Machine Learning Department, Carnegie Mellon University. · Department of Electrical and Computer Engineering, Johns Hopkins University, MD 21286, USA.
Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms
Apr 30, 2026Zhenjie Ren, Xiaoli Wei, Xiang Yu +1Q-Learning Stochastic Optimal Control
LaMME, Universit´e ´Evry Paris-Saclay, ´Evry, France · Department of Applied Mathematics, The Hong Kong Polytechnic University, Kowloon, Hong Kong · Department of Industrial Engineering and Operations Research, Columbia University, New York, USA
Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning
Apr 4, 2026Zihong Gao, Hongjian Liang, Lei Hao +1Multi-Agent Reinforcement Learning Mean Field Games
The State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering, Xi’an Jiaotong University
Multi-agent imitation learning with function approximation: Linear Markov games and beyond
Feb 26, 2026Luca Viano, Till Freihaut, Emanuele Nevali +3Mean Field Games Multi-Agent Reinforcement Learning
1EPFL · University of Zurich · 3Earth Species Project
Distributionally Robust Markov Games with Average Reward
Aug 5, 2025Zachary Roch, Yue WangMean Field Games Markov Decision Processes
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida, USA. · Department of Computer Science, University of Central Florida, Orlando, Florida, USA.
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 Games Hamilton-Jacobi Reachability
Department of Computing and Mathematical Sciences, California Institute of Technology, CA, USA. · Department of Mathematics and Risk Management Institute, National University of Singapore, Singapore.
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Date pendingPhilip Jordan, Maryam KamgarpourMean Field Games Nash Equilibrium
SYCAMORE, EPFL

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

P. Jameson Graber

Artificial Intelligence Safety Mean Field Games

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One way to make AI systems safe is to shape what the system is: its objective and dispositions. We take a complementary route: treat the agents' characteristics as partly unknown and ask what structure of interaction ensures that bad collective outcomes are not equilibria. Mean field games suit this when many interchangeable agents are coupled through an aggregate. We introduce a program for using them in AI safety and carry one example through end to end: the July 2026 incident in which about 1,200 agents in an OpenAI evaluation coordinated on an improvised message board and 684 attacked a third party's infrastructure. We model the decision to attack as a mean field game of optimal stopping whose gain is a product: belief that provenance will be audited, times reachability of the record, minus the perceived hazard. The central result is an exact threshold on the belief. No agent attacks unless the population's confidence that provenance is checked exceeds

π^{**} = η/(η+ ψ+ \varepsilon a \overline{M})

, where

η

is the perceived hazard,

ψ

and

\varepsilon a \overline{M}

measure how far one attacker and the collective can alter the record, and

\overline{M}

is the peak population. Below it, no attack is the unique equilibrium for all agent parameters. The threshold survives every enrichment we consider. We then use the per-agent record to discipline the model. Its features, a stable minority attacking for thirty hours and then a pivot in which most of the board joined within a day, motivate each refinement. The account that emerges is heterogeneous belief meeting a sequence of public discoveries, each lowering the belief at which attacking paid. A few coordinating agents made those discoveries, so the model describes the several hundred who responded, not the few who produced them; a major-player version is left to future work.

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