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Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation.

08:00 EDT 19th March 2020 | BioPortfolio

Summary of "Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation."

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 the kernelized Lipschitz estimation to learn multiplier matrices that are used in semidefinite programming frameworks for computing admissible initial control policies with provably high probability. Such admissible controllers enable safe initialization and constraint enforcement while providing exponential stability of the equilibrium of the closed-loop system.

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This article was published in the following journal.

Name: IEEE transactions on neural networks and learning systems
ISSN: 2162-2388
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