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- W3136756258 abstract "Kernel embeddings of distributions have recently gained significant attention in the machine learning community as a data-driven technique for representing probability distributions. Broadly, these techniques enable efficient computation of expectations by representing integral operators as elements in a reproducing kernel Hilbert space. We apply these techniques to the area of stochastic optimal control theory and present a method to compute approximately optimal policies for stochastic systems with arbitrary disturbances. Our approach reduces the optimization problem to a linear program, which can easily be solved via the Lagrangian dual, without resorting to gradient-based optimization algorithms. We focus on discrete- time dynamic programming, and demonstrate our proposed approach on a linear regulation problem, and on a nonlinear target tracking problem. This approach is broadly applicable to a wide variety of optimal control problems, and provides a means of working with stochastic systems in a data-driven setting." @default.
- W3136756258 created "2021-03-29" @default.
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- W3136756258 date "2021-12-14" @default.
- W3136756258 modified "2023-09-25" @default.
- W3136756258 title "Stochastic Optimal Control via Hilbert Space Embeddings of Distributions" @default.
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- W3136756258 doi "https://doi.org/10.1109/cdc45484.2021.9682801" @default.
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