Multi-agent Reinforcement Learning

Multi-Agent Reinforcement Learning is a subfield of reinforcement learning in which multiple autonomous agents learn optimal decision-making policies through interaction with a shared environment and with one another, distinguished from single-agent reinforcement learning by the inclusion of inter-agent dynamics such as cooperation, competition, or negotiation.

2K

Publications

127.1K

Citations

5.9K

Authors

1.3K

Institutions

Publications per year

2017–2026

1.6K

Authors

5.9K

  1. YY
    Yaodong Yang

    Peking University, China

    24 publications · 1.7K citations

    21

    h-index

  2. SW
    Shimon Whiteson

    University of Oxford, United Kingdom

    25 publications · 5.9K citations

    21

    h-index

  3. JF
    Jakob Foerster

    University of Oxford, United Kingdom

    20 publications · 5.4K citations

    19

    h-index

  4. KT
    Karl Tuyls

    Maastricht University, Netherlands

    22 publications · 1.9K citations

    18

    h-index

  5. JH
    Jianye Hao

    Tianjin University, China

    22 publications · 1.2K citations

    17

    h-index

1–5 of 5.9K

Venues

  1. arXiv (Cornell University)

    Repository · 243 publications · 15.4K citations

    54

    h-index

  2. IEEE Transactions on Vehicular Technology

    64 publications · 5.3K citations

    41

    h-index

  3. 34

    h-index

  4. Applied Energy

    38 publications · 2.8K citations

    31

    h-index

  5. IEEE Access

    52 publications · 3.1K citations

    28

    h-index

1–5