Graph-based Multi-agent Learning

Graph-Based Multi-Agent Learning is a multi-agent learning framework in which the interactions, dependencies, or communication structures among agents are explicitly modeled as a graph, enabling the use of graph-based representations and algorithms to facilitate coordinated decision-making and learning.

86

Publications

4K

Citations

318

Authors

128

Institutions

Publications per year

2017–2026

74

Authors

318

  1. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    6 publications · 246 citations

    6

    h-index

  2. BY
    Bicheng Ying

    École Polytechnique Fédérale de Lausanne, Switzerland

    3 publications · 159 citations

    3

    h-index

  3. SD
    Shifei Ding

    China University of Mining and Technology, China

    2 publications · 57 citations

    2

    h-index

  4. YG
    Yang Gao

    Nanjing University, China

    2 publications · 252 citations

    2

    h-index

  5. RL
    Rongpeng Li

    Zhejiang University, China

    2 publications · 92 citations

    2

    h-index

1–5 of 318

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 183 citations

    8

    h-index

  2. 7

    h-index

  3. 3

    h-index

  4. IEEE Transactions on Vehicular Technology

    3 publications · 112 citations

    3

    h-index

  5. Applied Intelligence

    2 publications · 88 citations

    2

    h-index

1–5