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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.|
|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.|
|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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