To refer to this page use:
|Abstract:||We study optimal regret bounds for control in linear dynamical systems under adversarially changing strongly convex cost functions, given the knowledge of transition dynamics. This includes several well studied and influential frameworks such as the Kalman filter and the linear quadratic regulator. State of the art methods achieve regret which scales as T^0.5, where T is the time horizon. We show that the optimal regret in this fundamental setting can be significantly smaller, scaling as polylog(T). This regret bound is achieved by two different efficient iterative methods, online gradient descent and online natural gradient.|
|Citation:||Agarwal, Naman, Elad Hazan, and Karan Singh. "Logarithmic Regret for Online Control." Advances in Neural Information Processing Systems 32 (2019).|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Advances in Neural Information Processing Systems|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.