Multi-agent Learning
Multi-Agent Learning is a computational framework within multi-agent systems that enables autonomous agents to adapt their behaviors through interaction with environments and other agents by jointly optimizing individual or collective objectives.
3.3K
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181.2K
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8.7K
Authors
1.9K
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2017–2026
1.8K
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Parents
3.3K
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, I. Babuschkin, Wojciech Marian Czarnecki et al. · Nature · 2019 · 3.4K citations
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Nanyang Technological University
Singapore, Singapore
33 h-index
133 publications
7.3K citations
Carnegie Mellon University
Pittsburgh, United States
32 h-index
115 publications
8.5K citations
Tsinghua University
Beijing, China
32 h-index
160 publications
9.7K citations

Shanghai Jiao Tong University
Shanghai, China
29 h-index
133 publications
8.4K citations

Beijing University of Posts and Telecommunications
Beijing, China
26 h-index
104 publications
4.8K citations
Massachusetts Institute of Technology
Cambridge, United States
26 h-index
88 publications
4.5K citations

Chinese Academy of Sciences
Beijing, China
25 h-index
94 publications
4.8K citations
University College London
London, United Kingdom
24 h-index
68 publications
4.1K citations
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Repository · 339 publications · 20.6K citations
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49 publications · 4.2K citations
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Conference · 60 publications · 5.5K citations
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34 publications · 3.5K citations
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34 publications · 2.4K citations
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