Function Approximation Error

Function Approximation Error is the discrepancy between the true value function and its estimated representation in a parametric function approximator, arising from the limitations of the approximator's capacity, architecture, or training process in accurately capturing the underlying reward structure within Discounted Reward Optimization.

678

Publications

51.3K

Citations

1.4K

Authors

620

Institutions

Publications per year

2017–2026

168

Authors

1.4K

  1. RM
    Rémi Munos

    Google (United States), United States

    8 publications · 603 citations

    8

    h-index

  2. AA
    Alekh Agarwal

    Microsoft (United States), United States

    7 publications · 287 citations

    7

    h-index

  3. MU
    Michaël Unser

    École Polytechnique Fédérale de Lausanne, Switzerland

    7 publications · 2.1K citations

    7

    h-index

  4. SM
    Shie Mannor

    Technion – Israel Institute of Technology, Israel

    5 publications · 222 citations

    5

    h-index

  5. YC
    Yu‐Ming Chu

    Huzhou University, China

    4 publications · 130 citations

    4

    h-index

1–5 of 1.4K

Venues

  1. arXiv (Cornell University)

    Repository · 49 publications · 3.9K citations

    22

    h-index

  2. IEEE Transactions on Neural Networks

    21 publications · 10.1K citations

    18

    h-index

  3. Neural Networks

    20 publications · 1.6K citations

    18

    h-index

  4. Mathematics of Computation

    MCOM · 23 publications · 2K citations

    16

    h-index

  5. 13

    h-index

1–5