Year: 2019
Type: preprint
Abstract: We consider the problem of controlling an unknown linear dynamical system in the presence of (nonstochastic) adversarial perturbations and adversarial convex loss functions. In contrast to classical control, the a priori determination of an optimal controller here is hindered by the latter's dependence on the yet unknown perturbations and costs. Instead, we measure regret against an optimal linear policy in hindsight, and give the first efficient algorithm that guarantees a sublinear regret bound, scaling as T^{2/3}, in this setting.
Source: arXiv (Cornell University)
Authors: Hazan, Elad, Kakade, Sham M., Singh, Karan
Institutions: Princeton University, University of Washington
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Awards: G1717380892, G8876996369
OpenAlex ID: W2990304163