mlsanityData to 7 Oct 2026

Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics

arXiv 2203.01170 ↗First posted 2 Mar 2022math.OC cs.LG stat.MLcited by 4

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Abstract (arXiv)

We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains an optimal √T regret-rate compared to the best stabilizing linear controller in hindsight. In contrast to previous work, our algorithm is based on the Optimism in the Face of Uncertainty paradigm. This results in a substantially improved computational complexity and a simpler analysis.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

17 of 31 references.

References · 17

  1. 2020Naive Exploration is Optimal for Online LQRcited by 111
  2. 2019Online Control with Adversarial Disturbancescited by 92
  3. 2018Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulatorcited by 86
  4. 2020Improper Learning for Non-Stochastic Controlcited by 83
  5. 2019Learning Linear-Quadratic Regulators Efficiently with only √T Regretcited by 67
  6. 2018Online Linear Quadratic Controlcited by 55
  7. 2020Logarithmic Regret Bound in Partially Observable Linear Dynamical Systemscited by 46
  8. 2020Black-Box Control for Linear Dynamical Systemscited by 43
  9. 2020Logarithmic Regret for Adversarial Online Controlcited by 41
  10. 2019Logarithmic Regret for Online Controlcited by 38
  11. 2020Logarithmic Regret for Learning Linear Quadratic Regulators Efficientlycited by 33
  12. 2018Smoothed Online Optimization for Regression and Controlcited by 26
5 more
  1. 2020Making Non-Stochastic Control (Almost) as Easy as Stochasticcited by 24
  2. 2020Non-Stochastic Control with Bandit Feedbackcited by 17
  3. 2013Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systemscited by 13
  4. 2020Bandit Linear Controlcited by 10
  5. 2020Geometric Exploration for Online Controlcited by 9

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2022Rate-Optimal Online Convex Optimization in Adaptive Linear Controlcited by 5
  2. 2023Online Nonstochastic Control with Adversarial and Static Constraintscited by 5
  3. 2025Scalable LinUCB: Low-Rank Design Matrix Updates for Recommenders with Large Action Spacescited by 1
  4. 2022Learning to Control under Time-Varying Environmentcited by 0