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- W3159697917 abstract "Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-agentproblems but mostly lack theoretical guarantees. Recently,mean field control and mean field games have been established as a tractable solution for large-scale multi-agent problems with many agents. In this work, driven by a motivating scheduling problem, we consider a discrete-time mean field control model with common environment states. We rigorously establish approximate optimality as the number of agents grows in the finite agent case and find that a dynamicprogramming principle holds, resulting in the existence ofan optimal stationary policy. As exact solutions are difficultin general due to the resulting continuous action space ofthe limiting mean field Markov decision process, we applyestablished deep reinforcement learning methods to solve theassociated mean field control problem. The performance ofthe learned mean field control policy is compared to typicalmulti-agent reinforcement learning approaches and is found toconverge to the mean field performance for sufficiently manyagents, verifying the obtained theoretical results and reachingcompetitive solutions" @default.
- W3159697917 created "2021-05-10" @default.
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- W3159697917 date "2021-04-30" @default.
- W3159697917 modified "2023-10-01" @default.
- W3159697917 title "Discrete-Time Mean Field Control with Environment States" @default.
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