ICML 2020poster17 citations
Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu
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.
BibTeX
@InProceedings{pmlr-v119-agarwal20b,
title = {Boosting for Control of Dynamical Systems},
author = {Agarwal, Naman and Brukhim, Nataly and Hazan, Elad and Lu, Zhou},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {96--103},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/agarwal20b/agarwal20b.pdf},
url = {https://proceedings.mlr.press/v119/agarwal20b.html},
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.}
}