Multi-robot Reinforcement Learning

Multi-Robot Reinforcement Learning is a subfield of reinforcement learning in which a single learning framework enables multiple robotic agents to cooperatively acquire optimal control policies through environment interaction, distinguished by joint decision-making, partial observability, and credit assignment challenges arising from decentralized execution and shared objectives.

84

Publications

3.5K

Citations

249

Authors

89

Institutions

Publications per year

2017–2026

41

Variants

Mrrl

Authors

249

  1. DG
    Dongbing Gu

    University of Essex, United Kingdom

    3 publications · 182 citations

    3

    h-index

  2. HM
    Hung Manh La

    University of Nevada, Reno, United States

    3 publications · 195 citations

    3

    h-index

  3. AP
    Amanda Prorok

    University of Cambridge, United Kingdom

    3 publications · 375 citations

    3

    h-index

  4. EY
    Erfu Yang

    University of Strathclyde, United Kingdom

    3 publications · 182 citations

    3

    h-index

  5. YW
    Ying Wang

    University of British Columbia, Canada

    2 publications · 92 citations

    2

    h-index

1–5 of 249

Venues

  1. IEEE Robotics and Automation Letters

    4 publications · 560 citations

    4

    h-index

  2. Sensors

    3 publications · 69 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 91 citations

    3

    h-index

  4. Journal of Robotics and Mechatronics

    2 publications · 25 citations

    2

    h-index

  5. 2

    h-index

1–5