Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics
Abstract
We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains an optimal $\sqrt{T}$ regret-rate against the best stabilizing linear controller. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.
Cite
Text
Cassel et al. "Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics." Conference on Learning Theory, 2022.Markdown
[Cassel et al. "Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics." Conference on Learning Theory, 2022.](https://mlanthology.org/colt/2022/cassel2022colt-efficient/)BibTeX
@inproceedings{cassel2022colt-efficient,
title = {{Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics}},
author = {Cassel, Asaf B and Cohen, Alon and Koren, Tomer},
booktitle = {Conference on Learning Theory},
year = {2022},
pages = {3589-3604},
volume = {178},
url = {https://mlanthology.org/colt/2022/cassel2022colt-efficient/}
}