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On the Convergence of Model Free Learning in Mean Field Games
[article]
2020
arXiv
pre-print
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
arXiv:1907.02633v3
fatcat:enhfu4622newvf2ey4q4vajiqm