Computing Stabilizing Linear Controllers via Policy Iteration

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


In recent years, a wide number of theoretical papers have focused on reinforcement learning approaches to the linear quadratic regulator (LQR) problem. However, nearly all of these papers assume that an initial stabilizing controller is given. This paper gives a model-free, off-policy reinforcement learning algorithm for computing a stabilizing controller for deterministic LQR problems with unknown dynamics and cost matrices. When the system is stabilizable, a controller which is guaranteed to stabilize the system is computed after finitely many steps. Furthermore, the solution converges to the optimal LQR gain.

Original languageEnglish (US)
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728174471
StatePublished - Dec 14 2020
Externally publishedYes
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference59th IEEE Conference on Decision and Control, CDC 2020
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island

Bibliographical note

Funding Information:
This work was supported in part by NSF CMMI-1727096 The author is with the department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA

Publisher Copyright:
© 2020 IEEE.


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