Multi-agent Imitation Learning
Multi-Agent Imitation Learning is a machine learning framework in which multiple autonomous agents learn coordinated policies by jointly mimicking expert behaviors from demonstration data, distinguishing itself from single-agent or non-imitative multi-agent learning through its reliance on observational supervision to acquire collaborative strategies.
40
Publications
2.1K
Citations
164
Authors
69
Institutions
Publications per year
2017–2026
38
40
1–5 of 40
164
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69

South China University of Technology
Guangzhou, China
4 h-index
7 publications
281 citations
Chongqing University of Posts and Telecommunications
Chongqing, China
3 h-index
8 publications
793 citations
National University of Singapore
Singapore, Singapore
3 h-index
9 publications
872 citations
Stanford University
Stanford, United States
3 h-index
13 publications
906 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
6 publications
1.6K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
2 h-index
3 publications
393 citations
China Electric Power Research Institute
Beijing, China
2 h-index
2 publications
35 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
2 h-index
4 publications
208 citations
1–8 of 69
Repository · 8 publications · 293 citations
8
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3 publications · 656 citations
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TMCCI · 2 publications · 173 citations
2
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2 publications · 55 citations
2
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2 publications · 109 citations
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1–5