Reward Decomposition

Reward Decomposition is a methodological framework in reward design that decomposes a composite reward signal into interpretable and actionable sub-rewards to facilitate learning, credit assignment, and alignment in complex decision-making environments.

11

Publications

922

Citations

36

Authors

19

Institutions

Publications per year

2017–2026

8

Authors

36

  1. MB
    Margaret Burnett

    Oregon State University, United States

    2 publications · 77 citations

    2

    h-index

  2. EN
    Evan Newman

    Oregon State University, United States

    2 publications · 77 citations

    2

    h-index

  3. AS
    Amrita Sadarangani

    Oregon State University, United States

    2 publications · 77 citations

    2

    h-index

  4. ZJ
    Zoe Juozapaitis

    Oregon State University, United States

    2 publications · 77 citations

    2

    h-index

  5. AF
    Alan Fern

    Oregon State University, United States

    2 publications · 77 citations

    2

    h-index

1–5 of 36

Venues

  1. International Conference on Machine Learning

    Conference · 1 publications · 129 citations

    1

    h-index

  2. Journal of Personality and Social Psychology

    1 publications · 205 citations

    1

    h-index

  3. IEEE Access

    1 publications · 32 citations

    1

    h-index

  4. ACM Transactions on Interactive Intelligent Systems

    TIIS · 1 publications · 64 citations

    1

    h-index

  5. 1

    h-index

1–5