Credit Assignment

Credit Assignment is a fundamental problem in reinforcement learning that pertains to the determination of how much responsibility each action or state is attributed for the observed rewards in a sequential decision-making process, distinguishing it from related problems by its focus on distributing credit or blame across a sequence of decisions to guide future policy improvement.

331

Publications

26K

Citations

982

Authors

390

Institutions

Publications per year

2017–2026

159

Authors

982

  1. KT
    Kagan Tumer

    Ames Research Center, United States

    9 publications · 247 citations

    9

    h-index

  2. YB
    Yoshua Bengio

    Université de Montréal, Canada

    9 publications · 1.2K citations

    9

    h-index

  3. ÁF
    Álvaro Fialho

    Institut national de recherche en sciences et technologies du numérique, France

    6 publications · 387 citations

    6

    h-index

  4. RJ
    Raymond J. Dolan

    University College London, United Kingdom

    7 publications · 441 citations

    6

    h-index

  5. MF
    Matthew F. S. Rushworth

    University of Oxford, United Kingdom

    5 publications · 867 citations

    5

    h-index

1–5 of 982

Venues

  1. arXiv (Cornell University)

    Repository · 46 publications · 3.3K citations

    21

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 2K citations

    10

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 7 publications · 195 citations

    7

    h-index

  4. Neural Computation

    7 publications · 2K citations

    7

    h-index

  5. Journal of Neuroscience

    7 publications · 438 citations

    7

    h-index

1–5