18 citations · 40 across the 5 of their papers we have counts for
6 papers
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Laurière, Sarah Perrin, Sertan Girgin +8
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs…
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…
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Thomas Anthony, Tom Eccles, Andrea Tacchetti +11
Recent advances in deep reinforcement learning (RL) have led to considerable progress in many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial natur…
On the Convergence of Model Free Learning in Mean Field Games
Romuald Elie, Julien Pérolat, Mathieu Laurière +2
Learning by experience in Multi-Agent Systems (MAS) is a difficult and exciting task, due to the lack of stationarity of the environment, whose dynamics evolves as the population l…