A data-driven α-policy iteration algorithm for optimal leader-following consensus of discrete-time multi-agent systems

In this paper, the data-driven α-policy iteration (PI) algorithm is proposed to address the optimal leader-following consensus problem of discrete-time multi-agent systems (MASs). Unlike existing results for state consensus problem that utilise the PI algorithm, the novel algorithm leverages only th...

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Veröffentlicht in:	International journal of systems science Jg. 56; H. 16; S. 4055 - 4072
Hauptverfasser:	Xiang, Aoxue, Zhao, Xinyuan, Ma, Ruicheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Taylor & Francis 10.12.2025 Taylor & Francis Ltd
Schlagworte:	Bellman theory Discrete time systems Iterative algorithms Linear quadratic regulator model-free Multi-agent systems Multiagent systems reinforcement learning state consensus α-policy iteration
ISSN:	0020-7721, 1464-5319
Online-Zugang:	Volltext
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