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- W3046611666 abstract "Author(s): Lin, Alex Tong | Advisor(s): Osher, Stanley J | Abstract: In this dissertation, we provide efficient algorithms for modeling the behavior of a single agent, multiple agents, and a continuum of agents. For a single agent, we combine the modeling framework of optimal control with advances in optimization splitting in order to efficiently find optimal paths for problems in very high-dimensions, thus providing alleviation from the curse of dimensionality. For a multiple, but finite, number of agents, we take the framework of multi-agent reinforcement learning and utilize imitation learning in order to decentralize a centralized expert, thus obtaining optimal multi-agents that act in a decentralized fashion. For a continuum of agents, we take the framework of mean-field games and use two neural networks, which we train in an alternating scheme, in order to efficiently find optimal paths for high-dimensional and stochastic problems. These tools cover a wide variety of use-cases that can be immediately deployed for practical applications." @default.
- W3046611666 created "2020-08-07" @default.
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- W3046611666 date "2020-01-01" @default.
- W3046611666 modified "2023-09-27" @default.
- W3046611666 title "Algorithms for Optimal Paths of One, Many, and an Infinite Number of Agents" @default.
- W3046611666 hasPublicationYear "2020" @default.
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