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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Vydané v:	IEEE transactions on information theory Ročník 71; číslo 7; s. 5601 - 5619
Hlavní autori:	Panda, Saunak Kumar, Liu, Ruiqi, Xiang, Yisha
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2025
Predmet:	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
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