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Learning in Domain Randomization via Continuous Time Non-Stochastic Control

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Publication:6439071

arXiv2306.01952MaRDI QIDQ6439071

Author name not available (Why is that?)

Publication date: 2 June 2023

Abstract: We study online control for continuous-time linear systems with finite sampling rates, where the objective is to design an online procedure that learns under non-stochastic noise and performs comparably to a fixed optimal linear controller. We present a novel two-level online algorithm, by integrating a higher-level learning strategy and a lower-level feedback control strategy. This method offers a practical and robust solution for online control, which achieves sublinear regret. Our work provides one of the first nonasymptotic results for controlling continuous-time linear systems a with finite number of interactions with the system.












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