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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. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Neural Information Processing Foundation , La Jolla, CA, pp. 1-11. ISBN 9781713829546.

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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:Book Section
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URLURL TypeDescription ItemDiscussion Paper
Yu, Chenkai0000-0001-8683-7773
Shi, Guanya0000-0002-9075-3705
Chung, Soon-Jo0000-0002-6657-3907
Yue, Yisong0000-0001-9127-1989
Wierman, Adam0000-0002-5923-0199
Additional Information:This project was supported in part by funding from Raytheon, DARPA PAI, AitF-1637598 and CNS-1518941, with additional support for Guanya Shi provided by the Simoudis Discovery Prize. We see no ethical concerns related to the results in this paper.
Funding AgencyGrant Number
Raytheon CompanyUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Simoudis Discovery PrizeUNSPECIFIED
Record Number:CaltechAUTHORS:20221222-184439362
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118583
Deposited By: George Porter
Deposited On:22 Dec 2022 23:56
Last Modified:22 Dec 2022 23:56

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