Behavior Reasoning for Opponent Agents in Multi-Agent Learning Systems-Reference-Cited by-同舟云学术

Behavior Reasoning for Opponent Agents in Multi-Agent Learning Systems

Published:2022-10 Issue:5 Volume:6 Page:1125-1136
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Hou Yaqing¹^ORCID,Sun Mingyang¹,Zhu Wenxuan¹,Zeng Yifeng²^ORCID,Piao Haiyin³^ORCID,Chen Xuefeng⁴^ORCID,Zhang Qiang¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Dalian University of Technology (DLUT), Dalian, China

2. Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.

3. School of electronics and information, Northwestern Polytechnical University, Xi’an, China

4. College of Computer Science, Chongqing University, Chongqing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Liaoning Province United Foundation

Fundamental Research Funds for the Central Universities

LiaoNing Revitalization Talents Program

Key Research and Development Program of Liaoning Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Link

Reference38 articles.

5. Using trajectory data to improve Bayesian optimization for reinforcement learning;wilson;J Mach Learn Res,2014

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