Approximate Value Iteration

Approximate Value Iteration is a variant of the value iteration algorithm in reinforcement learning that replaces exact value function updates with approximate ones, typically using function approximation to handle large or continuous state spaces.

81

Publications

5.3K

Citations

163

Authors

92

Institutions

Publications per year

2017–2026

24

Authors

163

  1. RM
    Rémi Munos

    Google (United States), United States

    6 publications · 378 citations

    6

    h-index

  2. AF
    Amir‐massoud Farahmand

    University of Alberta, Canada

    4 publications · 259 citations

    4

    h-index

  3. BS
    Bruno Scherrer

    Institut Élie Cartan de Lorraine, France

    4 publications · 121 citations

    4

    h-index

  4. MG
    Matthieu Geist

    Google (United States), United States

    4 publications · 131 citations

    4

    h-index

  5. SM
    Shie Mannor

    Technion – Israel Institute of Technology, Israel

    4 publications · 239 citations

    4

    h-index

1–5 of 163

Venues

  1. PolyPublie (École Polytechnique de Montréal)

    Repository · 6 publications · 301 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 231 citations

    5

    h-index

  3. Annals of Operations Research

    2 publications · 108 citations

    2

    h-index

  4. IEEE Transactions on Automatic Control

    2 publications · 34 citations

    2

    h-index

  5. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 2 publications · 91 citations

    2

    h-index

1–5