Riccati Updates for Online Linear Quadratic Control
Abstract
We study an online setting of the linear quadratic Gaussian optimal control problem on a sequence of cost functions, where similar to classical online optimization, the future decisions are made by only knowing the cost in hindsight. We introduce a modified online Riccati update that under some boundedness assumptions, leads to logarithmic regret bounds, improving the best known square-root bound. In particular, for the scalar case we achieve the logarithmic regret without any boundedness assumption. As opposed to earlier work, proposed method does not rely on solving semi-definite programs at each stage.
Cite
Text
Akbari et al. "Riccati Updates for Online Linear Quadratic Control." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Akbari et al. "Riccati Updates for Online Linear Quadratic Control." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/akbari2020l4dc-riccati/)BibTeX
@inproceedings{akbari2020l4dc-riccati,
title = {{Riccati Updates for Online Linear Quadratic Control}},
author = {Akbari, Mohammad and Gharesifard, Bahman and Linder, Tamas},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {476-485},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/akbari2020l4dc-riccati/}
}