No-regret Dynamics

No-Regret Dynamics is a learning framework in game theory whereby a player’s strategy update rule ensures that the long-term average regret of its actions converges to zero relative to the best fixed action in hindsight, under repeated interactions in a zero-sum game.

26

Publications

1.3K

Citations

65

Authors

39

Institutions

Publications per year

2017–2026

10

Variants

No-regret Learning

Authors

65

  1. GP
    Georgios Piliouras

    Singapore University of Technology and Design, Singapore

    3 publications · 79 citations

    3

    h-index

  2. GF
    Gabriele Farina

    Carnegie Mellon University, United States

    3 publications · 43 citations

    3

    h-index

  3. NG
    Nicola Gatti

    Politecnico di Milano, Italy

    2 publications · 27 citations

    2

    h-index

  4. AC
    Andrea Celli

    Politecnico di Milano, Italy

    2 publications · 27 citations

    2

    h-index

  5. AM
    Alberto Marchesi

    Politecnico di Milano, Italy

    2 publications · 27 citations

    2

    h-index

1–5 of 65

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 78 citations

    4

    h-index

  2. 2

    h-index

  3. Journal of the ACM

    JACM · 1 publications · 11 citations

    1

    h-index

  4. Mathematics of Operations Research

    1 publications · 110 citations

    1

    h-index

  5. Energy Policy

    1 publications · 29 citations

    1

    h-index

1–5