Convergence and Sample Complexity of Policy Gradient Methods for Stabilizing Linear Systems

System stabilization via policy gradient (PG) methods has drawn increasing attention in both control and machine learning communities. In this paper, we study their convergence and sample complexity for stabilizing linear time-invariant systems in terms of the number of system rollouts. Our analysis...

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Veröffentlicht in:	IEEE transactions on automatic control 2024-09, p.1-12
Hauptverfasser:	Zhao, Feiran, Fu, Xingyun, You, Keyou
Format:	Artikel
Sprache:	eng
Schlagworte:	Complexity theory Convergence Costs Linear systems Policy gradient sample complexity Search problems stabilization of linear systems the discounted LQR Trajectory Vectors
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