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Online Control with Adversarial Disturbances

Author(s): Agarwal, Naman; Bullins, Brian; Hazan, Elad; Kakade, Sham; Singh, Karan

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Abstract: 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.
Publication Date: 2019
Citation: Agarwal, Naman, Brian Bullins, Elad Hazan, Sham Kakade, and Karan Singh. "Online Control with Adversarial Disturbances." In Proceedings of the 36th International Conference on Machine Learning (2019): pp. 111-119.
ISSN: 2640-3498
Pages: 111 - 119
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the 36th International Conference on Machine Learning
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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