Robust Multi-agent Learning

Robust Multi-Agent Learning is a multi-agent learning framework that ensures consistent performance and convergence under environmental uncertainties, adversarial disturbances, and partial observability by explicitly incorporating resilience mechanisms into agents' decision-making processes.

40

Publications

2.9K

Citations

122

Authors

58

Institutions

Publications per year

2017–2026

31

Authors

122

  1. CA
    Christopher Amato

    Massachusetts Institute of Technology, United States

    3 publications · 226 citations

    3

    h-index

  2. JP
    Jason Pazis

    Duke University, United States

    2 publications · 205 citations

    2

    h-index

  3. BK
    Bhavya Kailkhura

    Lawrence Livermore National Laboratory, United States

    2 publications · 27 citations

    2

    h-index

  4. PK
    Pramod K. Varshney

    Syracuse University, United States

    2 publications · 27 citations

    2

    h-index

  5. MJ
    Ming Jin

    University of California, Berkeley, United States

    2 publications · 94 citations

    2

    h-index

1–5 of 122

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 1.3K citations

    9

    h-index

  2. IEEE Transactions on Intelligent Vehicles

    ITIVBL · 2 publications · 56 citations

    2

    h-index

  3. IEEE Transactions on Signal Processing

    2 publications · 62 citations

    2

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5