@inproceedings{384bdfc9a3f5410eba431f94f3180842,
title = "Online Linear Quadratic Control",
abstract = "We study the problem of controlling linear time- invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learning algorithms in this setting that guarantee O(Vf) regret under mild assumptions, where T is the time horizon. Our algorithms rely on a novel SDP relaxation for the steady-state distribution of the system. Crucially, and in contrast to previously proposed relaxations, the feasible solutions of our SDP all correspond to {"}strongly stable{"} policies that mix exponentially fast to a steady state.",
author = "Alon Cohen and Avinatan Hassidim and Tomer Koren and Nevena Lazic and Yishay Mansour and Kunal Talwar",
note = "Publisher Copyright: {\textcopyright} 2018 35th International Conference on Machine Learning, ICML 2018. All rights reserved.; 35th International Conference on Machine Learning, ICML 2018 ; Conference date: 10-07-2018 Through 15-07-2018",
year = "2018",
language = "אנגלית",
series = "35th International Conference on Machine Learning, ICML 2018",
publisher = "International Machine Learning Society (IMLS)",
pages = "1667--1681",
editor = "Andreas Krause and Jennifer Dy",
booktitle = "35th International Conference on Machine Learning, ICML 2018",
}