A Survey on Transformers in Reinforcement Learning
Machine Learning
2023-09-22 v3 Artificial Intelligence
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.
Keywords
Cite
@article{arxiv.2301.03044,
title = {A Survey on Transformers in Reinforcement Learning},
author = {Wenzhe Li and Hao Luo and Zichuan Lin and Chongjie Zhang and Zongqing Lu and Deheng Ye},
journal= {arXiv preprint arXiv:2301.03044},
year = {2023}
}
Comments
Accepted by TMLR