Multi-agent Robot Learning
Multi-Agent Robot Learning is a subfield of Robot Learning that focuses on the acquisition of coordinated behaviors through experience by multiple robotic agents interacting within a shared environment, distinguished by the necessity of handling inter-agent dependencies, joint decision-making, and decentralized control.
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2017–2026
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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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Carnegie Mellon University
Pittsburgh, United States
19 h-index
57 publications
4.2K citations
University of California, Berkeley
Berkeley, United States
15 h-index
46 publications
6.5K citations
Massachusetts Institute of Technology
Cambridge, United States
13 h-index
31 publications
1.6K citations
University of Pennsylvania
Philadelphia, United States
12 h-index
36 publications
1.3K citations
University of Southern California
Los Angeles, United States
12 h-index
24 publications
1.5K citations
The University of Osaka
Osaka, Japan
12 h-index
35 publications
1.4K citations
Tsinghua University
Beijing, China
12 h-index
37 publications
1.9K citations
Georgia Institute of Technology
Atlanta, United States
10 h-index
36 publications
2.2K citations
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Repository · 101 publications · 7.4K citations
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