Multi-robot Reinforcement Learning
Multi-Robot Reinforcement Learning is a subfield of reinforcement learning in which a single learning framework enables multiple robotic agents to cooperatively acquire optimal control policies through environment interaction, distinguished by joint decision-making, partial observability, and credit assignment challenges arising from decentralized execution and shared objectives.
84
Publications
3.5K
Citations
249
Authors
89
Institutions
Publications per year
2017–2026
41
Variants
Mrrl
Parents
84
Reinforcement Learning in the Multi-Robot Domain
Maja J. Matarić · Autonomous Robots · 1997 · 410 citations
1–5 of 84
249
3
h-index
3
h-index
3
h-index
3
h-index
2
h-index
1–5 of 249
89
University of Cambridge
Cambridge, United Kingdom
4 h-index
9 publications
1.2K citations

University of Essex
Colchester, United Kingdom
4 h-index
5 publications
428 citations

Harbin Institute of Technology
Harbin, China
3 h-index
7 publications
275 citations

University of British Columbia
Vancouver, Canada
3 h-index
6 publications
210 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
11 publications
378 citations

University of Georgia
Athens, United States
2 h-index
3 publications
68 citations

Stevens Institute of Technology
Hoboken, United States
2 h-index
3 publications
61 citations

Vaughn College of Aeronautics and Technology
New York, United States
2 h-index
4 publications
192 citations
1–8 of 89
4 publications · 560 citations
4
h-index
3 publications · 69 citations
3
h-index
Repository · 3 publications · 91 citations
3
h-index
2 publications · 25 citations
2
h-index
Conference · 2 publications · 37 citations
2
h-index
1–5