Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
The work proposes a data-driven method to find optimal control for nonlinear systems where dynamics and costs are unknown, using only control penalties. It embeds state probabilities into a Reproducing Kernel Hilbert Space (RKHS) to learn Markov transition operators from data. This allows the use of scalable operator-theoretic Hamilton-Jacobi-Bellman recursions to solve complex problems like autonomous underwater vehicle depth regulation.