Online Control with Adversarial Disturbances
Naman Agarwal · Brian Bullins · Elad Hazan · Sham Kakade · Karan Singh

Thu Jun 13th 06:30 -- 09:00 PM @ Pacific Ballroom #155

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.

Author Information

Naman Agarwal (Google AI Princeton)
Brian Bullins (Princeton University)
Elad Hazan (Princeton University)
Sham Kakade (University of Washington)
Karan Singh (Princeton University)

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