Multi-agent Robot Learning

Multi-Agent Robot Learning is a subfield of Robot Learning that focuses on the acquisition of coordinated behaviors through experience by multiple robotic agents interacting within a shared environment, distinguished by the necessity of handling inter-agent dependencies, joint decision-making, and decentralized control.

673

Publications

39.1K

Citations

2.1K

Authors

534

Institutions

Publications per year

2017–2026

404

Authors

2.1K

  1. SW
    Shimon Whiteson

    University of Oxford, United Kingdom

    14 publications · 3.5K citations

    14

    h-index

  2. MV
    Manuela Veloso

    Carnegie Mellon University, United States

    17 publications · 759 citations

    13

    h-index

  3. MA
    Minoru Asada

    The University of Osaka, Japan

    11 publications · 455 citations

    11

    h-index

  4. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    11 publications · 2K citations

    11

    h-index

  5. PS
    Peter Stone

    The University of Texas at Austin, United States

    9 publications · 687 citations

    9

    h-index

1–5 of 2.1K

Venues

  1. arXiv (Cornell University)

    Repository · 101 publications · 7.4K citations

    41

    h-index

  2. IEEE Robotics and Automation Letters

    24 publications · 1.4K citations

    19

    h-index

  3. 14

    h-index

  4. IEEE Access

    14 publications · 729 citations

    13

    h-index

  5. Information Sciences

    9 publications · 321 citations

    9

    h-index

1–5