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Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret

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arxiv 1902.06223 v2 pith:EIEBSFFO submitted 2019-02-17 cs.LG stat.ML

Alon Cohen , Tomer Koren , Yishay Mansour This is my paper · ORCID
classification cs.LGstat.ML
keywords learningregretsqrtabbasi-yadkorialgorithmcomputationally-efficientcontroldean
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abstract

We present the first computationally-efficient algorithm with $\widetilde O(\sqrt{T})$ regret for learning in Linear Quadratic Control systems with unknown dynamics. By that, we resolve an open question of Abbasi-Yadkori and Szepesv\'ari (2011) and Dean, Mania, Matni, Recht, and Tu (2018).

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Regret-Guaranteed Safe Switching: LQR Setting with Unknown Dynamics

    eess.SY 2026-06 unverdicted novelty 7.0 of 10

    Proposes a regret-minimizing algorithm for safe mode switching in unknown-dynamics LQR that achieves expected regret O(|M|^{1/4} n_s^{3/4} + n_m) under infinite mode visits.

  2. Multi-agent imitation learning with function approximation: Linear Markov games and beyond

    cs.LG 2026-02 conditional novelty 7.0 of 10

    In linear Markov games, behavior cloning's sample complexity hinges on a feature-level concentrability coefficient, and the interactive algorithm LSVI-UCB-ZERO-BC removes concentrability dependence entirely, scaling o...

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