Asymptotic Analysis of Sample-Averaged Q-Learning

Reinforcement learning (RL) has emerged as a key approach for training agents in complex and uncertain environments. Incorporating statistical inference in RL algorithms is essential for understanding and managing uncertainty in model performance. This paper introduces a generalized framework for ti...

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Veröffentlicht in:	IEEE transactions on information theory Jg. 71; H. 7; S. 5601 - 5619
Hauptverfasser:	Panda, Saunak Kumar, Liu, Ruiqi, Xiang, Yisha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.07.2025
Schlagworte:	Accuracy Approximation algorithms asymptotic normality Convergence Estimation Inference algorithms Q-learning random scaling Reinforcement learning (RL) Reliability statistical inference Stochastic processes Training Uncertainty
ISSN:	0018-9448, 1557-9654
Online-Zugang:	Volltext
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