SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods
This toolbox addresses stochastic optimal control problems for systems with nonlinear dynamics, black-box elements, and poorly characterized disturbances, but it appears incremental as it builds on existing kernel method techniques.
The authors introduced SOCKS, a data-driven stochastic optimal control toolbox using kernel methods to compute approximate solutions for problems with arbitrary cost and constraint functions, including stochastic reachability, and demonstrated its capabilities on several benchmarks.
We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel methods. SOCKS is a collection of data-driven algorithms that compute approximate solutions to stochastic optimal control problems with arbitrary cost and constraint functions, including stochastic reachability, which seeks to determine the likelihood that a system will reach a desired target set while respecting a set of pre-defined safety constraints. Our approach relies upon a class of machine learning algorithms based in kernel methods, a nonparametric technique which can be used to represent probability distributions in a high-dimensional space of functions known as a reproducing kernel Hilbert space. As a nonparametric technique, kernel methods are inherently data-driven, meaning that they do not place prior assumptions on the system dynamics or the structure of the uncertainty. This makes the toolbox amenable to a wide variety of systems, including those with nonlinear dynamics, black-box elements, and poorly characterized stochastic disturbances. We present the main features of SOCKS and demonstrate its capabilities on several benchmarks.