39 citations · 93 across the 10 of their papers we have counts for
5 papers · 1 filter
Solving N-player dynamic routing games with congestion: a mean field approach
Theophile Cabannes, Mathieu Lauriere, Julien Perolat +7
The recent emergence of navigational tools has changed traffic patterns and has now enabled new types of congestion-aware routing control like dynamic road pricing. Using the funda…
Generalization in Mean Field Games by Learning Master Policies
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
Mean Field Games (MFGs) can potentially scale multi-agent systems to extremely large populations of agents. Yet, most of the literature assumes a single initial distribution for th…
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
Matthieu Geist, Julien Pérolat, Mathieu Laurière +5
Concave Utility Reinforcement Learning (CURL) extends RL from linear to concave utilities in the occupancy measure induced by the agent's policy. This encompasses not only RL but a…
Mean Field Games Flock! The Reinforcement Learning Way
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
We present a method enabling a large number of agents to learn how to flock, which is a natural behavior observed in large populations of animals. This problem has drawn a lot of i…
Scaling up Mean Field Games with Online Mirror Descent
Julien Perolat, Sarah Perrin, Romuald Elie +5
We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash equilibriu…