SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods
Adam J. Thorpe,
Meeko M. K. Oishi
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 ker… Show more
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