SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods

• URL: http://arxiv.org/abs/2203.06290v1
• Date: Sat, 12 Mar 2022 00:09:08 GMT
• Title: SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods
• Authors: Adam J. Thorpe, Meeko M. K. Oishi
• Abstract summary: SOCKS is a data-driven optimal control toolbox based in kernel methods. We present the main features of SOCKS and demonstrate its capabilities on several benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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