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Feb, 2024
在线基于模型的Q学习的有限时间误差分析与放松采样模型
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model
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Han-Dong Lim, HyeAnn Lee, Donghwan Lee
TL;DR
通过理论分析和实证评估，本文探讨了当集成模型为基础的方法时，$Q$-学习在样本复杂度方面相对其无模型对应物而言的样本效率的条件。
Abstract
reinforcement learning
has witnessed significant advancements, particularly with the emergence of
model-based
approaches. Among these, $Q$-learning has proven to be a powerful algorithm in model-free settings. Ho
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