Competitive Control with Delayed Imperfect Information
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Abstract (arXiv)
This paper studies the impact of imperfect information in online control with adversarial disturbances. In particular, we consider both delayed state feedback and inexact predictions of future disturbances. We introduce a greedy, myopic policy that yields a constant competitive ratio against the offline optimal policy. We also analyze the fundamental limits of online control with limited information by showing that our competitive ratio bounds for the greedy, myopic policy in the adversarial setting match (up to lower-order terms) lower bounds in the stochastic setting.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
8 papers.
- 2021Online Optimization with Feedback Delay and Nonlinear Switching Costcited by 5
- 2022Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Controlcited by 5
- 2022Learning-Augmented Private Algorithms for Multiple Quantile Releasecited by 5
- 2021Robust Online Control with Model Misspecificationcited by 4
- 2023Adaptive Online Non-stochastic Controlcited by 3
- 2024Optimistic Online Non-stochastic Control via FTRLcited by 3
- 2022Optimal Competitive-Ratio Controlcited by 2
- 2026Constrained Online Convex Optimization with Memory and Predictionscited by 0
- 2026Constrained Online Convex Optimization with Memory and Predictions22 Mar 2026
- 2024Optimistic Online Non-stochastic Control via FTRL4 Apr 2024
- 2023Adaptive Online Non-stochastic Control2 Oct 2023
- 2022Learning-Augmented Private Algorithms for Multiple Quantile Release20 Oct 2022
- 2022Optimal Competitive-Ratio Control3 Jun 2022
- 2022Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control2 Jun 2022
- 2021Online Optimization with Feedback Delay and Nonlinear Switching Cost29 Oct 2021
- 2021Robust Online Control with Model Misspecification16 Jul 2021