Safe Multi-agent Learning

Safe Multi-Agent Learning is a subfield of Safe Robot Learning that focuses on the development of cooperative or competitive decision-making frameworks in which multiple autonomous agents learn policies that collectively satisfy safety constraints while achieving individual or shared objectives in uncertain environments.

28

Publications

1.4K

Citations

103

Authors

49

Institutions

Publications per year

2017–2026

23

Authors

103

  1. YY
    Yaodong Yang

    Peking University, China

    3 publications · 152 citations

    3

    h-index

  2. AK
    Alois Knoll

    Technical University of Munich, Germany

    2 publications · 137 citations

    2

    h-index

  3. AP
    Alexander Peysakhovich

    Yale University, United States

    2 publications · 41 citations

    2

    h-index

  4. JG
    Jakub Grudzien Kuba

    King's College London, United Kingdom

    2 publications · 137 citations

    2

    h-index

  5. AL
    Adam Lerer

    Meta (Israel), Israel

    2 publications · 41 citations

    2

    h-index

1–5 of 103

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 594 citations

    11

    h-index

  2. International Journal of Robust and Nonlinear Control

    IJRCEA · 1 publications · 111 citations

    1

    h-index

  3. Games and Economic Behavior

    1 publications · 157 citations

    1

    h-index

  4. IRIS Research product catalog (Sapienza University of Rome)

    Repository · 1 publications · 12 citations

    1

    h-index

  5. Neurocomputing

    1 publications · 18 citations

    1

    h-index

1–5