Multi-robot Policy Learning
Multi-Robot Policy Learning is a machine learning framework that enables a coordinated policy to be learned across multiple robotic agents through shared experiences and decentralized decision-making, distinguishing it from single-agent or centralized multi-agent policy learning approaches.
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Publications
2.6K
Citations
143
Authors
51
Institutions
Publications per year
2017–2026
28
Parents
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Reinforcement Learning in the Multi-Robot Domain
Maja J. Matarić · Autonomous Robots · 1997 · 410 citations
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Carnegie Mellon University
Pittsburgh, United States
4 h-index
11 publications
1.8K citations
University of Southern California
Los Angeles, United States
3 h-index
4 publications
1K citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
6 publications
292 citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
6 publications
114 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
1 h-index
1 publications
402 citations

Nankai University
Tianjin, China
1 h-index
3 publications
228 citations

Beijing Institute of Technology
Beijing, China
1 h-index
2 publications
94 citations

Sun Yat-sen University
Guangzhou, China
1 h-index
4 publications
184 citations
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Repository · 6 publications · 217 citations
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4 publications · 103 citations
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3 publications · 223 citations
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3 publications · 51 citations
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Conference · 2 publications · 80 citations
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