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Online Control with Adversarial Disturbances

Naman Agarwal
Brian Anderson Bullins
Elad Hazan
Sham Kakade
Karan Singh
ICML (2019)

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 that our model allows for adversarial noise in the dynamics and allows for general convex costs.