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Boosting for Control of Dynamical Systems

Author(s): Agarwal, Naman; Brukhim, Nataly; Hazan, Elad; Lu, Zhou

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Abstract: We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online control. Our main result is an efficient boosting algorithm that combines weak controllers into a provably more accurate one. Empirical evaluation on a host of control settings supports our theoretical findings.
Publication Date: 2020
Citation: Agarwal, Naman, Nataly Brukhim, Elad Hazan, and Zhou Lu. "Boosting for Control of Dynamical Systems." In Proceedings of the 37th International Conference on Machine Learning (2020): pp. 96-103.
ISSN: 2640-3498
Pages: 96 - 103
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the 37th International Conference on Machine Learning
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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