Multi-agent Learning

Multi-Agent Learning is a computational framework within multi-agent systems that enables autonomous agents to adapt their behaviors through interaction with environments and other agents by jointly optimizing individual or collective objectives.

3.3K

Publications

181.2K

Citations

8.7K

Authors

1.9K

Institutions

Publications per year

2017–2026

1.8K

Variants

Mal

Authors

8.7K

  1. SW
    Shimon Whiteson

    University of Oxford, United Kingdom

    32 publications · 6.1K citations

    25

    h-index

  2. YY
    Yaodong Yang

    Peking University, China

    27 publications · 2.1K citations

    22

    h-index

  3. KT
    Karl Tuyls

    Maastricht University, Netherlands

    25 publications · 2.4K citations

    21

    h-index

  4. JH
    Jianye Hao

    Tianjin University, China

    28 publications · 1.4K citations

    19

    h-index

  5. PS
    Peter Stone

    The University of Texas at Austin, United States

    21 publications · 1.5K citations

    18

    h-index

1–5 of 8.7K

Venues

  1. arXiv (Cornell University)

    Repository · 339 publications · 20.6K citations

    67

    h-index

  2. IEEE Transactions on Vehicular Technology

    49 publications · 4.2K citations

    36

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 60 publications · 5.5K citations

    33

    h-index

  4. 31

    h-index

  5. Applied Energy

    34 publications · 2.4K citations

    29

    h-index

1–5