Boosting for Control of Dynamical Systems
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.
Cite
Text
Agarwal et al. "Boosting for Control of Dynamical Systems." International Conference on Machine Learning, 2020.Markdown
[Agarwal et al. "Boosting for Control of Dynamical Systems." International Conference on Machine Learning, 2020.](https://mlanthology.org/icml/2020/agarwal2020icml-boosting/)BibTeX
@inproceedings{agarwal2020icml-boosting,
title = {{Boosting for Control of Dynamical Systems}},
author = {Agarwal, Naman and Brukhim, Nataly and Hazan, Elad and Lu, Zhou},
booktitle = {International Conference on Machine Learning},
year = {2020},
pages = {96-103},
volume = {119},
url = {https://mlanthology.org/icml/2020/agarwal2020icml-boosting/}
}