Online Control with Adversarial Disturbances
Machine Learning
2019-02-26 v1 Systems and Control
Optimization and Control
Machine Learning
Abstract
We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise). The objective we consider is one of regret: we desire an online control procedure that can do nearly as well as that of a procedure that has full knowledge of the disturbances in hindsight. Our main result is an efficient algorithm that provides nearly tight regret bounds for this problem. From a technical standpoint, this work generalizes upon previous work in two main aspects: our model allows for adversarial noise in the dynamics, and allows for general convex costs.
Cite
@article{arxiv.1902.08721,
title = {Online Control with Adversarial Disturbances},
author = {Naman Agarwal and Brian Bullins and Elad Hazan and Sham M. Kakade and Karan Singh},
journal= {arXiv preprint arXiv:1902.08721},
year = {2019}
}