Multi-agent Curriculum Learning

Multi-Agent Curriculum Learning is a structured learning framework in which the training of multiple interacting agents is progressively organized through a sequence of tasks or environments of increasing complexity to facilitate coordinated skill acquisition and convergence in multi-agent systems.

27

Publications

1.2K

Citations

93

Authors

33

Institutions

Publications per year

2017–2026

25

Authors

93

  1. SN
    Sanmit Narvekar

    The University of Texas at Austin, United States

    3 publications · 208 citations

    3

    h-index

  2. CC
    Cédric Colas

    Institut Systèmes Intelligents et de Robotique, France

    3 publications · 84 citations

    3

    h-index

  3. PS
    Peter Stone

    The University of Texas at Austin, United States

    3 publications · 208 citations

    3

    h-index

  4. ZL
    Zeming Lin

    Meta (Israel), Israel

    2 publications · 222 citations

    2

    h-index

  5. PO
    Pierre‐Yves Oudeyer

    Institut national de recherche en sciences et technologies du numérique, France

    2 publications · 67 citations

    2

    h-index

1–5 of 93

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 280 citations

    6

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 136 citations

    2

    h-index

  3. IEEE Transactions on Vehicular Technology

    1 publications · 180 citations

    1

    h-index

  4. International Conference on Learning Representations

    Conference · 1 publications · 80 citations

    1

    h-index

  5. IEEE Transactions on Robotics

    1 publications · 35 citations

    1

    h-index

1–5