Safe Multi-agent Learning
Safe Multi-Agent Learning is a subfield of Safe Robot Learning that focuses on the development of cooperative or competitive decision-making frameworks in which multiple autonomous agents learn policies that collectively satisfy safety constraints while achieving individual or shared objectives in uncertain environments.
28
Publications
1.4K
Citations
103
Authors
49
Institutions
Publications per year
2017–2026
23
Parents
28
Playing safe in coordination games:
David Schmidt, Robert Shupp, James M. Walker · Games and Economic Behavior · 2003 · 157 citations
Safe multi-agent reinforcement learning for multi-robot control
Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen et al. · Artificial Intelligence · 2023 · 121 citations · Full text
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University of Oxford
Oxford, United Kingdom
3 h-index
7 publications
253 citations
The University of Texas at Austin
Austin, United States
2 h-index
3 publications
77 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
3 publications
156 citations
King's College London
London, United Kingdom
2 h-index
2 publications
137 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
1 publications
111 citations

North China Electric Power University
Beijing, China
1 h-index
1 publications
28 citations

Federal Trade Commission
Washington D.C., United States
1 h-index
1 publications
157 citations
Guangdong Institute of Intelligent Manufacturing
Guangzhou, China
1 h-index
3 publications
54 citations
1–8 of 49
Repository · 11 publications · 594 citations
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IJRCEA · 1 publications · 111 citations
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1 publications · 157 citations
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Repository · 1 publications · 12 citations
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1 publications · 18 citations
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