Mean Field Learning

Mean Field Learning is a computational framework within Multi-Agent Robot Learning that approximates each agent's interaction with a large population by modeling the collective behavior of all agents as a continuous mean field, thereby simplifying decentralized decision-making in high-dimensional multi-agent systems.

139

Publications

7.7K

Citations

309

Authors

170

Institutions

Publications per year

2017–2026

86

Authors

309

  1. ML
    Mathieu Laurière

    Princeton University, United States

    12 publications · 340 citations

    11

    h-index

  2. ZH
    Zhu Han

    University of Houston, United States

    6 publications · 215 citations

    6

    h-index

  3. OP
    Olivier Pietquin

    Supélec, France

    6 publications · 132 citations

    6

    h-index

  4. RÉ
    Romuald Élie

    Centre de Recherche en Mathématiques de la Décision, France

    6 publications · 132 citations

    6

    h-index

  5. MG
    Matthieu Geist

    Google (United States), United States

    6 publications · 132 citations

    6

    h-index

1–5 of 309

Venues

  1. arXiv (Cornell University)

    Repository · 24 publications · 1K citations

    16

    h-index

  2. Neural Information Processing Systems

    Conference · 6 publications · 274 citations

    6

    h-index

  3. Neural Computation

    4 publications · 703 citations

    4

    h-index

  4. International Conference on Machine Learning

    Conference · 3 publications · 200 citations

    3

    h-index

  5. IEEE Transactions on Cybernetics

    3 publications · 92 citations

    3

    h-index

1–5