mlsanityData to 7 Oct 2026

Non-Stochastic Control with Bandit Feedback

arXiv 2008.05523 ↗First posted 12 Aug 2020cs.LG math.OC stat.MLcited by 17

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Abstract (arXiv)

We study the problem of controlling a linear dynamical system with adversarial perturbations where the only feedback available to the controller is the scalar loss, and the loss function itself is unknown. For this problem, with either a known or unknown system, we give an efficient sublinear regret algorithm. The main algorithmic difficulty is the dependence of the loss on past controls. To overcome this issue, we propose an efficient algorithm for the general setting of bandit convex optimization for loss functions with memory, which may be of independent interest.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Aug 2020.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 7; 2024: 3; 2025: 2.

17 papers.

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  1. 2021Non-stationary Online Learning with Memory and Non-stochastic Controlcited by 33
  2. 2023Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictionscited by 8
  3. 2024Improved Regret for Bandit Convex Optimization with Delayed Feedbackcited by 8
  4. 2023Optimal Rates for Bandit Nonstochastic Controlcited by 7
  5. 2022Optimal Dynamic Regret in LQR Controlcited by 6
  6. 2022Online Convex Optimization with Unbounded Memorycited by 6
  7. 2022Rate-Optimal Online Convex Optimization in Adaptive Linear Controlcited by 5
  8. 2023Online Nonstochastic Control with Adversarial and Static Constraintscited by 5
  9. 2022Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamicscited by 4
  10. 2023A Nonstochastic Control Approach to Optimizationcited by 4
  11. 2023Approximate Information States for Worst-Case Control and Learning in Uncertain Systemscited by 3
  12. 2023Adaptive Online Non-stochastic Controlcited by 3
5 more
  1. 2024Optimistic Online Non-stochastic Control via FTRLcited by 3
  2. 2023Online Nonstochastic Model-Free Reinforcement Learningcited by 2
  3. 2024Tight Rates for Bandit Control Beyond Quadraticscited by 2
  4. 2025Influential Bandits: Pulling an Arm May Change the Environmentcited by 0
  5. 2025Online Multi-Agent Control with Adversarial Disturbancescited by 0