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Algorithmic Optimality Guarantees for Nonsmooth $H_\infty$ Output-Feedback Policy Search
We study continuous-time full-order dynamic output-feedback $H_\infty$ policy search, a nonconvex and nonsmooth problem. Direct policy search is a central paradigm in reinforcement learning and continuous control, but rigorous guarantees remain scarce in robust output-feedback settings. The $H_\inft
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