Reward Learning

Reward Learning is a methodological framework within policy learning that specifies how an agent acquires and refines a reward function from environmental feedback or expert guidance to guide optimal decision-making.

603

Publications

44.8K

Citations

2.3K

Authors

743

Institutions

Publications per year

2017–2026

278

Authors

2.3K

  1. DA
    Diego A. Pizzagalli

    Harvard University, United States

    23 publications · 2.4K citations

    20

    h-index

  2. BA
    Brian A. Anderson

    Texas A&M University, United States

    11 publications · 1.1K citations

    11

    h-index

  3. SP
    Stefano Palminteri

    Laboratoire de Neurosciences Cognitives, France

    9 publications · 747 citations

    9

    h-index

  4. RJ
    Raymond J. Dolan

    University College London, United Kingdom

    8 publications · 2.3K citations

    8

    h-index

  5. ND
    Nathaniel D. Daw

    Princeton University, United States

    8 publications · 1.3K citations

    8

    h-index

1–5 of 2.3K

Venues

  1. Journal of Neuroscience

    41 publications · 5.5K citations

    36

    h-index

  2. arXiv (Cornell University)

    Repository · 30 publications · 1.3K citations

    17

    h-index

  3. NeuroImage

    12 publications · 1.1K citations

    12

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 12 publications · 2.6K citations

    12

    h-index

  5. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 12 publications · 369 citations

    12

    h-index

1–5