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The Power of Predictions in Online Control

Yu, Chenkai and Shi, Guanya and Chung, Soon-Jo and Yue, Yisong and Wierman, Adam (2020) The Power of Predictions in Online Control. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200707-094715120

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Abstract

We study the impact of predictions in online Linear Quadratic Regulator control with both stochastic and adversarial disturbances in the dynamics. In both settings, we characterize the optimal policy and derive tight bounds on the minimum cost and dynamic regret. Perhaps surprisingly, our analysis shows that the conventional greedy MPC approach is a near-optimal policy in both stochastic and adversarial settings. Specifically, for length-T problems, MPC requires only O(logT) predictions to reach O(1) dynamic regret, which matches (up to lower-order terms) our lower bound on the required prediction horizon for constant regret.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2006.07569arXivDiscussion Paper
ORCID:
AuthorORCID
Chung, Soon-Jo0000-0002-6657-3907
Yue, Yisong0000-0001-9127-1989
Record Number:CaltechAUTHORS:20200707-094715120
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200707-094715120
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104236
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Jul 2020 17:16
Last Modified:07 Jul 2020 17:16

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