A Survey on Transformers in Reinforcement Learning

Wenzhe Li, Hao Luo, Zichuan Lin, Chongjie Zhang, Zongqing Lu, Deheng Ye

Abstract: Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. In this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects.

Certifications: Survey Certification

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Edward_Grefenstette1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 1346