Boosting for Control of Dynamical Systems
Author(s): Agarwal, Naman; Brukhim, Nataly; Hazan, Elad; Lu, Zhou
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http://arks.princeton.edu/ark:/88435/pr15s05| 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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