Online Control with Adversarial Disturbances

Naman Agarwal · Brian Bullins · Elad Hazan · Sham M. Kakade · Karan Singh

102 citations0/0 files storedarXiv (Cornell University)

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Advanced Bandit Algorithms Research Advanced Control Systems Optimization Adaptive Dynamic Programming Control

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.

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