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 success 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, typical 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 Transformer research developments in RL for decision-making. We hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future directions. To keep track of the rapid TransRL developments in the decision-making domains, we summarize the latest papers and their open-source implementations at https://github.com/williamyuanv0/Transformer-in-Reinforcement-Learning-for-Decision-Making-A-Survey.

Key words: Transformer; Reinforcement learning (RL); Decision-making (DM); Deep neural network (DNN); Multi-agent reinforcement learning (MARL); Meta-reinforcement learning (Meta-RL)

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