Ensemble Dynamics Models

Ensemble Dynamics Models is a model-based reinforcement learning approach that comprises a collection of multiple probabilistic dynamics models, distinguished by its use of ensemble averaging and uncertainty estimation to improve robustness and generalization in learning system transition dynamics.

22

Publications

862

Citations

92

Authors

41

Institutions

Publications per year

2017–2026

9

Authors

92

  1. ER
    Ensor Rafael Palacios

    Wellcome Centre for Human Neuroimaging, United Kingdom

    1 publications · 62 citations

    1

    h-index

  2. NB
    Nusret Balci

    University of Minnesota, United States

    1 publications · 20 citations

    1

    h-index

  3. NM
    Natasha M. Maurits

    University of Groningen, Netherlands

    1 publications · 65 citations

    1

    h-index

  4. BC
    Bruno Chaudret

    Centre National de la Recherche Scientifique, France

    1 publications · 39 citations

    1

    h-index

  5. MT
    Masayoshi Takayanagi

    Nagoya University, Japan

    1 publications · 12 citations

    1

    h-index

1–5 of 92

Venues

  1. The Journal of Chemical Physics

    2 publications · 130 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 1 publications · 17 citations

    1

    h-index

  3. Physical Review A

    PRA · 1 publications · 176 citations

    1

    h-index

  4. New Journal of Physics

    NJP · 1 publications · 13 citations

    1

    h-index

  5. Physical Review E

    PRE · 1 publications · 13 citations

    1

    h-index

1–5