Multi-robot Policy Learning

Multi-Robot Policy Learning is a machine learning framework that enables a coordinated policy to be learned across multiple robotic agents through shared experiences and decentralized decision-making, distinguishing it from single-agent or centralized multi-agent policy learning approaches.

44

Publications

2.6K

Citations

143

Authors

51

Institutions

Publications per year

2017–2026

28

Authors

143

  1. PL
    Pinxin Long

    Fuzhou University, China

    3 publications · 636 citations

    3

    h-index

  2. JP
    Jia Pan

    University of Hong Kong, Hong Kong SAR China

    3 publications · 636 citations

    3

    h-index

  3. MJ
    Maja J. Matarić

    University of Southern California, United States

    3 publications · 127 citations

    3

    h-index

  4. WL
    Wenxi Liu

    Fuzhou University, China

    3 publications · 636 citations

    3

    h-index

  5. HW
    Hesheng Wang

    Shanghai Jiao Tong University, China

    2 publications · 144 citations

    2

    h-index

1–5 of 143

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 217 citations

    6

    h-index

  2. Journal of Intelligent & Robotic Systems

    4 publications · 103 citations

    4

    h-index

  3. IEEE Transactions on Industrial Informatics

    3 publications · 223 citations

    3

    h-index

  4. Sensors

    3 publications · 51 citations

    3

    h-index

  5. 2022 International Conference on Robotics and Automation (ICRA)

    Conference · 2 publications · 80 citations

    2

    h-index

1–5