Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

IF 10.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE transactions on neural networks and learning systems Pub Date : 2024-11-25 DOI:10.1109/tnnls.2024.3497992

Yuji Cao, Huan Zhao, Yuheng Cheng, Ting Shu, Yue Chen, Guolong Liu, Gaoqi Liang, Junhua Zhao, Jinyue Yan, Yun Li

引用次数: 0

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

大型语言模型增强强化学习调查：概念、分类和方法

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

CiteScore

23.80

自引率

9.60%

发文量

2102

审稿时长

3-8 weeks

期刊介绍： The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.

期刊最新文献

Take Your Pick: Enabling Effective Distributed Learning Within Low-Dimensional Feature Space A Survey on Knowledge Editing of Neural Networks GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Model Beyond Message-Passing: Generalization of Graph Neural Networks via Feature Perturbation for Semi-Supervised Node Classification Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods