Machine Learning
and
Mean Field Games
Seminar series
Online talks at the interface of
machine learning and mean field games
In the past few years, the connections between machine learning and mean field methods have emerged as a fruitful research direction. On the one hand, machine learning methods such as deep learning or reinforcement learning can be used to solve mean field games. On the other hand, mean field techniques can be used to study neural networks or multi-agent reinforcement learning. This seminar series aims at fostering the interactions on these topics.
Upcoming talks
TBA
Past talks
June 4, 2024
Bruno Ziliotto
Bayesian learning in Mean Field Games
May 7, 2024
Bruno Gaujal
Near-Optimal Policies for Weakly Coupled Markov Decision Processes
April 2, 2024
Bora Yongacoglu
Partially observed n-player mean-field games: Connections to POMDPs and Subjective Equilibrium
March 12, 2024
Batuhan Yardim
When is Mean-Field Reinforcement Learning Tractable and Relevant?
November 28, 2023
Gökçe Dayanikli
Utilizing deep learning and game theory to find optimal policies for societies
November 14, 2023
Desik Rengarajan
Reinforcement Learning for Mean Field Games with Strategic Complementarities
October 24, 2023
Benjamin Zhang
A mean-field games laboratory for generative modeling
September 19, 2023
Patrick Benjamin
Networked Communication for Decentralised Agents in Mean-Field Games
June 6, 2023
Barna Pasztor
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
May 16, 2023
Tamer Başar
Mean Field Games with Elements of Robustness and Learning
March 28, 2023
Niao He
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
March 7, 2023
Panayotis Mertikopoulos
Learning in nonatomic games
February 7, 2023
Yongzhao Wang
Empirical Game-Theoretic Analysis for Mean Field Games
January 10, 2023
Zhenjie Ren
Regularized Mean Field Optimization with Application to Neural Networks
December 6 2022
Yang Chen
Mean Field Game as a Framework for Many-agent Inverse Reinforcement Learning
November 22, 2022
Hamidou Tembine
Data-Driven Mean-Field-Type Games
November 8, 2022
Fabio Camilli
A Mean Field Games approach to cluster analysis
July 5, 2022
Sharon Di
Dynamic Driving and Routing Games for Autonomous Vehicles on Networks: A Mean Field Game Approach
June 21, 2022
Daniel Kious
Finding geodesics on graphs using reinforcement learning
May 31, 2022
Pascal Poupart
Extending Mean Field Reinforcement Learning to Partially Observable Environments, Agents of Multiple Types and Decentralized Learning
May 17, 2022
Andrea Angiuli
Bridging the gap of reinforcement learning for mean field games and mean field control problems
May 3, 2022
Muhammad Aneeq uz Zaman
Reinforcement Learning for Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games in Multiple Populations
March 22, 2022
Ruimeng Hu
Convergence of Empirical Measures, Mean-Field Games and Signatures
March 15, 2022
Mérouane Debbah
Distributed Network Design in the Era of Deep Learning
February 15, 2022
Naci Saldi
Learning in Discounted-cost and Average-cost Mean-field Games
January 18, 2022
François Delarue
Exploration Noise for learning linear-quadratic mean field games
December 14, 2021
Kai Cui
Learning Mean Field Games: Entropy Regularization and Dense Graph Limits
November 23, 2021
Aditya Mahajan
Reinforcement learning in stationary mean-field games
November 16, 2021
Sarah Perrin & Mathieu Laurière
Learning Mean Field Games and Mean Field Control: An Overview
November 2, 2021
Xin Guo
A mean field perspective for multi-agent reinforcement learning
Reach out if you want to participate !
Organisers:
Romuald Elie (DeepMind), Matthieu Geist (Google Brain),
Mathieu Laurière (NYU Shanghai, Google Brain), Sarah Perrin (University of Lille), Olivier Pietquin (Google Brain)
Contact: mathieu.lauriere@nyu.edu