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

Authors

161

  1. KT
    Kagan Tumer

    Ames Research Center, United States

    7 publications · 165 citations

    7

    h-index

  2. AA
    Adrian Agogino

    Ames Research Center, United States

    5 publications · 136 citations

    5

    h-index

  3. HZ
    Hongyuan Zha

    Georgia Institute of Technology, United States

    3 publications · 73 citations

    3

    h-index

  4. CZ
    Chongjie Zhang

    Tsinghua University, China

    3 publications · 82 citations

    3

    h-index

  5. MC
    Mitchell Colby

    Oregon State University, United States

    3 publications · 73 citations

    3

    h-index

1–5 of 161

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 829 citations

    10

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 1.7K citations

    2

    h-index

  3. 1

    h-index

  4. International Conference on Learning Representations

    Conference · 1 publications · 30 citations

    1

    h-index

  5. Frontiers in Psychology

    1 publications · 38 citations

    1

    h-index

1–5