[PDF][PDF] Learning mean field games: A survey
M Laurière, S Perrin, M Geist… - arXiv preprint arXiv …, 2022 - researchgate.net
Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …
applications but remain generally intractable when the number of players increases …
A novel framework for policy mirror descent with general parameterization and linear convergence
Modern policy optimization methods in reinforcement learning, such as TRPO and PPO, owe
their success to the use of parameterized policies. However, while theoretical guarantees …
their success to the use of parameterized policies. However, while theoretical guarantees …
Oracle-free reinforcement learning in mean-field games along a single sample path
We consider online reinforcement learning in Mean-Field Games (MFGs). Unlike traditional
approaches, we alleviate the need for a mean-field oracle by developing an algorithm that …
approaches, we alleviate the need for a mean-field oracle by developing an algorithm that …
Learning regularized monotone graphon mean-field games
This paper studies two fundamental problems in regularized Graphon Mean-Field Games
(GMFGs). First, we establish the existence of a Nash Equilibrium (NE) of any $\lambda …
(GMFGs). First, we establish the existence of a Nash Equilibrium (NE) of any $\lambda …
On imitation in mean-field games
We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs),
where the goal is to imitate the behavior of a population of agents following a Nash …
where the goal is to imitate the behavior of a population of agents following a Nash …
When is Mean-Field Reinforcement Learning Tractable and Relevant?
Mean-field reinforcement learning has become a popular theoretical framework for efficiently
approximating large-scale multi-agent reinforcement learning (MARL) problems exhibiting …
approximating large-scale multi-agent reinforcement learning (MARL) problems exhibiting …
On the statistical efficiency of mean-field reinforcement learning with general function approximation
In this paper, we study the fundamental statistical efficiency of Reinforcement Learning in
Mean-Field Control (MFC) and Mean-Field Game (MFG) with general model-based function …
Mean-Field Control (MFC) and Mean-Field Game (MFG) with general model-based function …
[PDF][PDF] Major-Minor Mean Field Multi-Agent Reinforcement Learning
Recently, mean field control (MFC) has provided a tractable and theoretically founded
approach to otherwise difficult cooperative multi-agent control. However, the strict …
approach to otherwise difficult cooperative multi-agent control. However, the strict …
Learning Discrete-Time Major-Minor Mean Field Games
Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-
player games with many similar, rational agents. However, standard MFGs remain limited to …
player games with many similar, rational agents. However, standard MFGs remain limited to …
Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective
In this paper, we study the problem of robust cooperative multi-agent reinforcement learning
(RL) where a large number of cooperative agents with distributed information aim to learn …
(RL) where a large number of cooperative agents with distributed information aim to learn …