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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
Weilin Yuan
National University of Defense Technology

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Jiaxing Chen
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Shaofei Chen
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Dawei Feng
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Zhenzhen Hu
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Weiwei Zhao

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Abstract

Reinforcement learning (RL) has become a dominant decision-making paradigm and has achieved notable success in many real-world applications, such as autonomous driving, robotic manipulation, robotic navigation and gaming AI. However, the powerful abilities of the RL variants were developed with advanced machine learning techniques. For example, deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making tasks. Inspired by present day 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 Transformerbased 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 decisionmaking. 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/Transformerin-Reinforcement-Learning-for-Decision-Making-A-Survey.