188 citations · 880 across the 70 of their papers we have counts for
4 papers · 1 filter
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua +3
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a dee…
Learning Discrete-Time Major-Minor Mean Field Games
Kai Cui, Gökçe Dayanıklı, Mathieu Laurière +3
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 ho…
Learning Correlated Equilibria in Mean-Field Games
Paul Muller, Romuald Elie, Mark Rowland +7
The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts. However, as the size of an -player…
Foolproof Cooperative Learning
Alexis Jacq, Julien Perolat, Matthieu Geist +1
This paper extends the notion of learning equilibrium in game theory from matrix games to stochastic games. We introduce Foolproof Cooperative Learning (FCL), an algorithm that con…