298 citations · 845 across the 46 of their papers we have counts for
6 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…
Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO
Paul Muller, Mark Rowland, Romuald Elie +6
Recent advances in multiagent learning have seen the introduction ofa family of algorithms that revolve around the population-based trainingmethod PSRO, showing convergence to Nash…
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…
Learning Nash Equilibrium for General-Sum Markov Games from Batch Data
Julien Pérolat, Florian Strub, Bilal Piot +1
This paper addresses the problem of learning a Nash equilibrium in -discounted multiplayer general-sum Markov Games (MG). A key component of this model is the possibility for th…