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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.|
|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.|
|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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