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Transformer in reinforcement learning for decision-making: a survey
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Weilin YUAN, Jiaxing CHEN, Shaofei CHEN, Dawei FENG,Zhenzhen HU, Peng LI, Weiwei ZHAO. Transformer in reinforcement learning for decision-making: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Transformer in reinforcement learning for decision-making: a survey",
author="Weilin YUAN, Jiaxing CHEN, Shaofei CHEN, Dawei FENG,Zhenzhen HU, Peng LI, Weiwei ZHAO",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300548"
}
%0 Journal Article
%T Transformer in reinforcement learning for decision-making: a survey
%A Weilin YUAN
%A Jiaxing CHEN
%A Shaofei CHEN
%A Dawei FENG
%A Zhenzhen HU
%A Peng LI
%A Weiwei ZHAO
%J Journal of Zhejiang University SCIENCE C
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%N -1
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300548
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T1 - Transformer in reinforcement learning for decision-making: a survey
A1 - Weilin YUAN
A1 - Jiaxing CHEN
A1 - Shaofei CHEN
A1 - Dawei FENG
A1 - Zhenzhen HU
A1 - Peng LI
A1 - Weiwei ZHAO
J0 - Journal of Zhejiang University Science C
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%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300548
Abstract: reinforcement learning (RL) has become a dominant decision-making paradigm and has achieved notable success in many real-world applications. Notably, deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making tasks. Inspired by current major successes of transformer in natural language processing and computer vision, numerous bottlenecks have been overcome by combining transformer with RL for decision-making. This paper presents a multiangle systematic survey of various transformer-based RL (TransRL) models applied in decision-making tasks, including basic models, advanced algorithms, representative implementation instances, applications, and known challenges. Our work aims to provide insights into problems that inherently arise with the current RL approaches, and examines how we can address them with better TransRL models. To our knowledge, we are the first to present a comprehensive review of the recent transformers research developments in RL for decision-making. We hope this survey provides a comprehensive review of TransRL models and also inspires the RL community in its pursuit of future directions. Finally, to keep track of the rapid TransRL developments in the decision-making domains, we summarize the latest relevant papers and their open-source implementations at https://github.com/williamyuanv0/transformer-in-Reinforcement-Learning-for-decision-making-A-Survey.
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