Poster
Online Control with Adversarial Disturbances
Naman Agarwal · Brian Bullins · Elad Hazan · Sham Kakade · Karan Singh
Pacific Ballroom #155
Keywords: [ Online Learning ] [ Planning and Control ] [ Theory and Algorithms ]
We study the control of linear dynamical systems with adversarial disturbances, as opposed to statistical noise. We present an efficient algorithm that achieves nearly-tight regret bounds in this setting. Our result generalizes upon previous work in two main aspects: the algorithm can accommodate adversarial noise in the dynamics, and can handle general convex costs.
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