Search Results for author:
Found 9 papers, 2 papers with code
Learning in Mean Field Games: A Survey
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.
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.
Generalization in Mean Field Games by Learning Master Policies
Mean Field Games (MFGs) can potentially scale multi-agent systems to extremely large populations of agents.
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint
Mean-field Games (MFGs) are a continuous approximation of many-agent RL.
Mean Field Games Flock! The Reinforcement Learning Way
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.
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
It also features a large combinatorial action space and simultaneous moves, which are challenging for RL algorithms.
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
On the Convergence of Model Free Learning in Mean Field Games
In order to design scalable algorithms for systems with a large population of interacting agents (e. g. swarms), this paper focuses on Mean Field MAS, where the number of agents is asymptotically infinite.
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents.
