On the Convergence of Model Free Learning in Mean Field Games

4 Jul 2019Romuald ElieJulien PérolatMathieu LaurièreMatthieu GeistOlivier Pietquin

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... (read more)

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