Neural Value Approximation

Neural Value Approximation is a value function approximation method in reinforcement learning that uses neural networks as the function approximator to estimate the value of states or state-action pairs, distinguishing itself from linear or table-based approximators by leveraging nonlinear representations for scalability and generalization in large or continuous state spaces.

5

Publications

694

Citations

20

Authors

9

Institutions

Publications per year

2017–2026

4

Authors

20

  1. IR
    Ian Reid

    The University of Adelaide, Australia

    2 publications · 184 citations

    2

    h-index

  2. BZ
    Bohan Zhuang

    Monash University, Australia

    2 publications · 184 citations

    2

    h-index

  3. LL
    Lingqiao Liu

    The University of Adelaide, Australia

    2 publications · 184 citations

    2

    h-index

  4. CS
    Chunhua Shen

    The University of Adelaide, Australia

    2 publications · 184 citations

    2

    h-index

  5. MT
    Mingkui Tan

    South China University of Technology, China

    2 publications · 184 citations

    2

    h-index

1–5 of 20

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 34 citations

    2

    h-index

  2. Nature

    1 publications · 476 citations

    1

    h-index

1–2