Competitive Robot Learning

Competitive Robot Learning is a subfield of Multi-Agent Robot Learning in which robotic agents autonomously acquire policies through learning algorithms that explicitly optimize individual performance under adversarial or rivalrous interactions, distinguishing it from cooperative or neutral multi-agent learning paradigms.

22

Publications

973

Citations

70

Authors

30

Institutions

Publications per year

2017–2026

8

Authors

70

  1. PS
    Peter Stone

    The University of Texas at Austin, United States

    3 publications · 172 citations

    3

    h-index

  2. LJ
    Long Jin

    Lanzhou University, China

    2 publications · 76 citations

    2

    h-index

  3. PS
    Peng Sun

    Tencent (China), China

    2 publications · 67 citations

    2

    h-index

  4. MS
    Mingsheng Shang

    Chongqing Institute of Green and Intelligent Technology, China

    2 publications · 76 citations

    2

    h-index

  5. TB
    Trapit Bansal

    University of Massachusetts Amherst, United States

    2 publications · 296 citations

    2

    h-index

1–5 of 70

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 308 citations

    3

    h-index

  2. AI Communications

    AICOM · 1 publications · 20 citations

    1

    h-index

  3. IEEE/CAA Journal of Automatica Sinica

    1 publications · 56 citations

    1

    h-index

  4. 1

    h-index

  5. Science China Technological Sciences

    1 publications · 33 citations

    1

    h-index

1–5