153 citations · 427 across the 46 of their papers we have counts for
5 papers · 1 filter
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
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…
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…
Learning in Mean Field Games: A Survey
Mathieu Laurière, Sarah Perrin, Julien Pérolat +5
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. Introduced b…
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…