Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation

3 Jul 2019Ankush ChakrabartyDevesh K. JhaGregery T. BuzzardYebin WangKyriakos Vamvoudakis

We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics. We employ kernelized Lipschitz estimation and semidefinite programming for computing admissible initial control policies with provably high probability... (read more)

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