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Almost Surely √T Regret for Adaptive LQR

arXiv 2301.05537 ↗First posted 13 Jan 2023math.OC cs.LG cs.SYcited by 0

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Abstract (arXiv)

The Linear-Quadratic Regulation (LQR) problem with unknown system parameters has been widely studied, but it has remained unclear whether tilde O(√T) regret, which is the best known dependence on time, can be achieved almost surely. In this paper, we propose an adaptive LQR controller with almost surely tilde O(√T) regret upper bound. The controller features a circuit-breaking mechanism, which circumvents potential safety breach and guarantees the convergence of the system parameter estimate, but is shown to be triggered only finitely often and hence has negligible effect on the asymptotic performance of the controller. The proposed controller is also validated via simulation on Tennessee Eastman Process~(TEP), a commonly used industrial process example.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 41 references that are in the map. First posted on arXiv 13 Jan 2023.

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10 of 41 references.

References · 10

  1. 2018Learning Without Mixing: Towards A Sharp Analysis of Linear System Identificationcited by 158
  2. 2020Naive Exploration is Optimal for Online LQRcited by 116
  3. 2018Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulatorcited by 86
  4. 2019Learning Linear-Quadratic Regulators Efficiently with only √T Regretcited by 70
  5. 2018On Adaptive Linear-Quadratic Regulatorscited by 21
  6. 2018Input Perturbations for Adaptive Control and Learningcited by 18
  7. 2022On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectorycited by 7
  8. 2020Exact Asymptotics for Linear Quadratic Adaptive Controlcited by 4
  9. 2019An Online Approach to Physical Watermark Designcited by 3
  10. 2022Safe and Efficient Switching Mechanism Design for Uncertified Linear Controllercited by 1

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