17 research outputs found
Data-Driven Stochastic Optimal Control Using Kernel Gradients
We present an empirical, gradient-based method for solving data-driven
stochastic optimal control problems using the theory of kernel embeddings of
distributions. By embedding the integral operator of a stochastic kernel in a
reproducing kernel Hilbert space, we can compute an empirical approximation of
stochastic optimal control problems, which can then be solved efficiently using
the properties of the RKHS. Existing approaches typically rely upon finite
control spaces or optimize over policies with finite support to enable
optimization. In contrast, our approach uses kernel-based gradients computed
using observed data to approximate the cost surface of the optimal control
problem, which can then be optimized using gradient descent. We apply our
technique to the area of data-driven stochastic optimal control, and
demonstrate our proposed approach on a linear regulation problem for comparison
and on a nonlinear target tracking problem