On the Convergence of Model Free Learning in Mean Field Games

Understand

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 learns.

• 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.
• Recently, a very active burgeoning field studies the effects of diverse reinforcement learning algorithms for agents with no prior information on a stationary Mean Field Game (MFG) and learn their policy through repeated experience.