<ul><li>Researchers address stability properties of the Hamilton — Jacobi — Bellman (HJB) equation in Lipschitz continuous optimal control problems for model-free reinforcement learning.</li><li>They bridge the gap between Lipschitz continuous optimal control problems and classical optimal control problems, exploring stability and convergence rates of value functions.</li><li>A generalized framework for Lipschitz continuous control problems is proposed, along with a new HJB-based reinforcement learning algorithm.</li><li>The proposed method is compared with existing approaches using benchmark examples.</li></ul>

Research on Lipschitz continuous concept in Machine Learning part1

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