Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics

Asaf Cassel, Alon Cohen, Tomer Koren

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)3589-3604
Number of pages16
JournalProceedings of Machine Learning Research
StatePublished - 2022
Event35th Conference on Learning Theory, COLT 2022 - London, United Kingdom
Duration: 2 Jul 20225 Jul 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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