Output feedback Q-learning for discrete-time linear zero-sum games with application to the \(H_\infty\) control
type: Item
instance of: scholarly article
title: Output feedback Q-learning for discrete-time linear zero-sum games with application to the \(H_\infty\) control
zbMATH Open document ID: 1402.93126
author: Syed Ali Asad Rizvi
author: Zongli Lin
published in: Automatica
publication date: 2018-11-21T00:00:00Z
Mathematics Subject Classification ID: 93B52
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 93C05
Mathematics Subject Classification ID: 93B36
Mathematics Subject Classification ID: 91A05
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 93C95
zbMATH DE Number: 6982630
zbMATH Keywords: reinforcement learning
zbMATH Keywords: ADP
zbMATH Keywords: Q-learning
zbMATH Keywords: zero-sum games
zbMATH Keywords: \(H_\infty\) control
zbMATH Keywords: output feedback
MaRDI profile type: Publication
full work available at URL: j.automatica.2018.05.027
OpenAlex ID: W2807176303
cites work: Model-free \(Q\)-learning designs for linear discrete-time zero-sum games with application to \(H^\infty\) control
cites work: \(\mathrm{H}_\infty\) control of linear discrete-time systems: off-policy reinforcement learning
cites work: Q4850020
cites work: Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming
cites work: Stability Analysis of Discrete-Time Infinite-Horizon Optimal Control With Discounted Cost
cites work: Adaptive dynamic programming for an online solution of a zero-sum differential game
cites work: \({\mathcal Q}\)-learning
DOI: 10.1016/J.AUTOMATICA.2018.05.027
description: scientific article; zbMATH DE number 6982630