Transformer in reinforcement learning for decision-making: a survey
Regular Papers|Updated:2024-07-11
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Transformer in reinforcement learning for decision-making: a survey
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基于Transformer的强化学习方法在智能决策领域的应用:综述
“In the field of decision-making, reinforcement learning (RL) has achieved significant success, with deep neural networks playing a key role. This paper presents a comprehensive survey of Transformer-based RL (TransRL) models, exploring basic models, advanced algorithms, and typical applications, providing solutions to address inherent problems in current RL approaches. The first of its kind, this review offers insights and inspiration for the RL community in pursuing future directions.”
Frontiers of Information Technology & Electronic EngineeringVol. 25, Issue 6, Pages: 763-790(2024)
Affiliations:
1.College of Information and Communication, National University of Defense Technology, Wuhan 430014, China
2.College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410072, China
3.Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha 410072, China
WEILIN YUAN, JIAXING CHEN, SHAOFEI CHEN, et al. Transformer in reinforcement learning for decision-making: a survey. [J]. Frontiers of information technology & electronic engineering, 2024, 25(6): 763-790.
DOI:
WEILIN YUAN, JIAXING CHEN, SHAOFEI CHEN, et al. Transformer in reinforcement learning for decision-making: a survey. [J]. Frontiers of information technology & electronic engineering, 2024, 25(6): 763-790. DOI: 10.1631/FITEE.2300548.
Transformer in reinforcement learning for decision-making: a surveyCover ArticleEnhanced Publication