On the Sample Complexity of Decentralized Linear Quadratic Regulator With Partially Nested Information Structure
From MaRDI portal
Publication:6093205
DOI10.1109/TAC.2022.3215940arXiv2110.07112MaRDI QIDQ6093205
Author name not available (Why is that?)
Publication date: 6 October 2023
Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)
Abstract: We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown. We propose a model-based learning solution, which consists of two steps. First, we estimate the unknown system model from a single system trajectory of finite length, using least squares estimation. Next, based on the estimated system model, we design a control policy that satisfies the desired information structure. We show that the suboptimality gap between our control policy and the optimal decentralized control policy (designed using accurate knowledge of the system model) scales linearly with the estimation error of the system model. Using this result, we provide an end-to-end sample complexity result for learning decentralized controllers for a linear quadratic control problem with a partially nested information structure.
Full work available at URL: https://arxiv.org/abs/2110.07112
optimal controlsystem identificationdecentralized controllarge-scale systemsreinforcement learningstatistical learning
Related Items (1)
This page was built for publication: On the Sample Complexity of Decentralized Linear Quadratic Regulator With Partially Nested Information Structure
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6093205)
