Visible to the public Biblio

Filters: Author is Oishi, Meeko M. K.  [Clear All Filters]
2022-10-20
Thorpe, Adam J., Oishi, Meeko M. K..  2021.  Stochastic Optimal Control via Hilbert Space Embeddings of Distributions. 2021 60th IEEE Conference on Decision and Control (CDC). :904—911.
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.