Online Control with Adversarial Disturbances
Author(s): Agarwal, Naman; Bullins, Brian; Hazan, Elad; Kakade, Sham; Singh, Karan
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http://arks.princeton.edu/ark:/88435/pr1hz7d| 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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