Multi-agent Credit Assignment
Multi-Agent Credit Assignment is a methodological framework in multi-agent robot learning that identifies and quantifies the individual contributions of each agent to the collective outcome, thereby enabling the attribution of rewards or penalties in a shared environment.
50
Publications
3.7K
Citations
161
Authors
60
Institutions
Publications per year
2017–2026
32
Parents
50
Counterfactual Multi-Agent Policy Gradients
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras et al. · arXiv (Cornell University) · 2017 · 478 citations · Full text
Evolutionary Reinforcement Learning: A Survey
Hui Bai, Ran Cheng, Yaochu Jin · Intelligent Computing · 2023 · 78 citations · Full text
1–5 of 50
161
7
h-index
5
h-index
3
h-index
3
h-index
3
h-index
1–5 of 161
60

Ames Research Center
Mountain View, United States
5 h-index
9 publications
261 citations
Oregon State University
Corvallis, United States
5 h-index
9 publications
211 citations
Tsinghua University
Beijing, China
4 h-index
11 publications
330 citations

Peking University
Beijing, China
2 h-index
2 publications
60 citations
University of Oxford
Oxford, United Kingdom
2 h-index
10 publications
10.6K citations
The University of Texas at Austin
Austin, United States
2 h-index
2 publications
64 citations
Chinese University of Hong Kong, Shenzhen
Shenzhen, China
2 h-index
2 publications
78 citations
Singapore Management University
Singapore, Singapore
2 h-index
6 publications
237 citations
1–8 of 60
Repository · 10 publications · 829 citations
10
h-index
Conference · 2 publications · 1.7K citations
2
h-index
TPAMI · 1 publications · 28 citations
1
h-index
Conference · 1 publications · 30 citations
1
h-index
1 publications · 38 citations
1
h-index
1–5