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Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are unknown, while only a control penalty function and constraints a
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