Scalable Multi-agent Learning

Scalable Multi-Agent Learning is a formal framework in multi-agent robot learning that enables the coordination and learning of a large number of agents by maintaining computational efficiency and performance consistency as the system size increases.

105

Publications

6.6K

Citations

405

Authors

117

Institutions

Publications per year

2017–2026

89

Authors

405

  1. CA
    Christopher Amato

    Massachusetts Institute of Technology, United States

    5 publications · 204 citations

    5

    h-index

  2. JW
    Jun Wang

    University College London, United Kingdom

    5 publications · 472 citations

    5

    h-index

  3. MB
    Michael Bowling

    University of Alberta, Canada

    4 publications · 172 citations

    4

    h-index

  4. YY
    Yaodong Yang

    Peking University, China

    4 publications · 573 citations

    4

    h-index

  5. GQ
    Guannan Qu

    Harvard University, United States

    4 publications · 87 citations

    4

    h-index

1–5 of 405

Venues

  1. arXiv (Cornell University)

    Repository · 34 publications · 2.9K citations

    23

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 509 citations

    9

    h-index

  3. IEEE Robotics and Automation Letters

    3 publications · 104 citations

    3

    h-index

  4. IEEE Access

    2 publications · 58 citations

    2

    h-index

  5. Adaptive Agents and Multi-Agents Systems

    Conference · 2 publications · 76 citations

    2

    h-index

1–5