Reward Normalization

Reward Normalization is a method in reward design that scales and adjusts reward signals to a consistent range or statistical distribution, enabling stable and efficient learning in reinforcement learning systems by mitigating the impact of reward magnitude variability.

10

Publications

477

Citations

28

Authors

25

Institutions

Publications per year

2017–2026

7

Authors

28

  1. KL
    Kenway Louie

    New York University, United States

    3 publications · 179 citations

    3

    h-index

  2. HV
    Hado van Hasselt

    Google DeepMind (United Kingdom), United Kingdom

    2 publications · 98 citations

    2

    h-index

  3. AG
    Arthur Guez

    Google DeepMind (United Kingdom), United Kingdom

    2 publications · 98 citations

    2

    h-index

  4. PW
    Paul W. Glimcher

    New York University, United States

    2 publications · 158 citations

    2

    h-index

  5. DS
    David Silver

    Google DeepMind (United Kingdom), United Kingdom

    2 publications · 98 citations

    2

    h-index

1–5 of 28

Venues

  1. Nature Communications

    2 publications · 158 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 98 citations

    2

    h-index

  3. European Journal of Operational Research

    1 publications · 67 citations

    1

    h-index

  4. PLoS Computational Biology

    1 publications · 21 citations

    1

    h-index

  5. eLife

    1 publications · 32 citations

    1

    h-index

1–5