Finite-Time Analysis of Simultaneous Double Q-learning

$Q$-learning is one of the most fundamental reinforcement learning (RL) algorithms. Despite its widespread success in various applications, it is prone to overestimation bias in the $Q$-learning update. To address this issue, double $Q$-learning employs two independent $Q$-estimators which are rando...

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Bibliographische Detailangaben

Hauptverfasser:	Na, Hyunjun, Lee, Donghwan
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Computer Science - Systems and Control
Online-Zugang:	Volltext bestellen
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