Reinforcement Learning For Sorting

Reinforcement Learning for Sorting is a computational framework within automated sorting that employs reinforcement learning to optimize sorting strategies by enabling an agent to learn sequential decision policies through interaction with an environment to maximize cumulative reward based on sorting efficiency.

8

Publications

407

Citations

56

Authors

8

Institutions

Publications per year

2017–2026

6

Authors

56

  1. PK
    Pushmeet Kohli

    Microsoft Research (United Kingdom), United Kingdom

    2 publications · 180 citations

    2

    h-index

  2. YL
    Yujia Li

    University of Toronto, Canada

    2 publications · 180 citations

    2

    h-index

  3. OV
    Oriol Vinyals

    Google (United States), United States

    2 publications · 180 citations

    2

    h-index

  4. LA
    Lamia A. Shihata

    German University in Cairo, Egypt

    1 publications · 23 citations

    1

    h-index

  5. AM
    Amol Mandhane

    Google (United States), United States

    1 publications · 161 citations

    1

    h-index

1–5 of 56

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 32 citations

    2

    h-index

  2. Preprints.org

    Repository · 1 publications · 10 citations

    1

    h-index

  3. IEEE Access

    1 publications · 23 citations

    1

    h-index

  4. Nature

    1 publications · 161 citations

    1

    h-index

  5. Journal of Experimental Psychology

    1 publications · 13 citations

    1

    h-index

1–5