中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 复杂系统认知与决策实验室
Large sequence models for sequential decision-making: a survey

文献类型：期刊论文

作者Wen, Muning1,2; Lin, Runji3,5; Wang, Hanjing1,2; Yang, Yaodong4; Wen, Ying1; Mai, Luo6; Wang, Jun2,7; Zhang, Haifeng3,5; Zhang, Weinan1
刊名FRONTIERS OF COMPUTER SCIENCE
出版日期2023-12-01
卷号17期号:6页码:18
ISSN号2095-2228
关键词sequential decision-making sequence modeling the Transformer training system
DOI10.1007/s11704-023-2689-5
通讯作者Zhang, Weinan(wnzhang@sjtu.edu.cn)
英文摘要Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Transformer. Although originally designed for prediction problems, it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems, which are typically beset by long-standing issues involving sample efficiency, credit assignment, and partial observability. In recent years, sequence models, especially the Transformer, have attracted increasing interest in the RL communities, spawning numerous approaches with notable effectiveness and generalizability. This survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer, by discussing the connection between sequential decision-making and sequence modeling, and categorizing them based on the way they utilize the Transformer. Moreover, this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making, encompassing theoretical foundations, network architectures, algorithms, and efficient training systems.yy
WOS关键词REINFORCEMENT ; LEVEL
资助项目New Generation of AI 2030 Major Project[2018AAA0100900] ; Shanghai Municipal Science and Technology Major Project[2021SHZDZX0102] ; National Natural Science Foundation of China[62076161] ; Wu Wen Jun Honorary Scholarship, AI Institute, Shanghai Jiao Tong University
WOS研究方向Computer Science
语种英语
出版者HIGHER EDUCATION PRESS
WOS记录号WOS:001042755200001
资助机构New Generation of AI 2030 Major Project ; Shanghai Municipal Science and Technology Major Project ; National Natural Science Foundation of China ; Wu Wen Jun Honorary Scholarship, AI Institute, Shanghai Jiao Tong University
源URL[http://ir.ia.ac.cn/handle/173211/53873]  
专题复杂系统认知与决策实验室
通讯作者Zhang, Weinan
作者单位1.Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200241, Peoples R China
2.Digital Brain Lab, Shanghai 201306, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Peking Univ, Inst Artificial Intelligence, Beijing 100091, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
6.Univ Edinburgh, Sch Informat, Edinburgh EH8 9JU, Scotland
7.UCL, Dept Comp Sci, London WC1E 6BT, England
推荐引用方式
GB/T 7714		 Wen, Muning,Lin, Runji,Wang, Hanjing,et al. Large sequence models for sequential decision-making: a survey[J]. FRONTIERS OF COMPUTER SCIENCE,2023,17(6):18.
APA Wen, Muning.,Lin, Runji.,Wang, Hanjing.,Yang, Yaodong.,Wen, Ying.,...&Zhang, Weinan.(2023).Large sequence models for sequential decision-making: a survey.FRONTIERS OF COMPUTER SCIENCE,17(6),18.
MLA Wen, Muning,et al."Large sequence models for sequential decision-making: a survey".FRONTIERS OF COMPUTER SCIENCE 17.6(2023):18.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。