Multi-agent Imitation Learning

Multi-Agent Imitation Learning is a machine learning framework in which multiple autonomous agents learn coordinated policies by jointly mimicking expert behaviors from demonstration data, distinguishing itself from single-agent or non-imitative multi-agent learning through its reliance on observational supervision to acquire collaborative strategies.

40

Publications

2.1K

Citations

164

Authors

69

Institutions

Publications per year

2017–2026

38

Authors

164

  1. GS
    Guillaume Sartoretti

    National University of Singapore, Singapore

    4 publications · 629 citations

    4

    h-index

  2. TY
    Tao Yu

    South China University of Technology, China

    3 publications · 81 citations

    3

    h-index

  3. BW
    Blake Wulfe

    Toyota Research Institute

    3 publications · 148 citations

    3

    h-index

  4. ZN
    Zhaolong Ning

    Dalian University of Technology, China

    3 publications · 310 citations

    3

    h-index

  5. JL
    Jiawen Li

    South China University of Technology, China

    3 publications · 81 citations

    3

    h-index

1–5 of 164

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 293 citations

    8

    h-index

  2. IEEE Robotics and Automation Letters

    3 publications · 656 citations

    3

    h-index

  3. IEEE Transactions on Mobile Computing

    TMCCI · 2 publications · 173 citations

    2

    h-index

  4. IEEE Access

    2 publications · 55 citations

    2

    h-index

  5. IEEE Transactions on Industrial Informatics

    2 publications · 109 citations

    2

    h-index

1–5