Learning Linear-Quadratic Regulators Efficiently with Only $\sqrt{T}$ Regret
Abstract
We present the first computationally-efficient algorithm with $\widetilde{O}(\sqrt{T})$ regret for learning in Linear Quadratic Control systems with unknown dynamics. By that, we resolve an open question of Abbasi-Yadkori and Szepesvari (2011) and Dean,Mania, Matni, Recht, and Tu (2018).
Cite
Text
Cohen et al. "Learning Linear-Quadratic Regulators Efficiently with Only $\sqrt{T}$ Regret." International Conference on Machine Learning, 2019.Markdown
[Cohen et al. "Learning Linear-Quadratic Regulators Efficiently with Only $\sqrt{T}$ Regret." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/cohen2019icml-learning/)BibTeX
@inproceedings{cohen2019icml-learning,
title = {{Learning Linear-Quadratic Regulators Efficiently with Only $\sqrt{T}$ Regret}},
author = {Cohen, Alon and Koren, Tomer and Mansour, Yishay},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {1300-1309},
volume = {97},
url = {https://mlanthology.org/icml/2019/cohen2019icml-learning/}
}