Reinforcement Learning For Composition

Reinforcement Learning for Composition is a computational framework within algorithmic composition that employs reinforcement learning techniques to iteratively optimize musical sequences through agent-environment interactions, where rewards guide the generation of aesthetically or stylistically coherent music.

29

Publications

1.1K

Citations

79

Authors

34

Institutions

Publications per year

2017–2026

14

Authors

79

  1. HW
    Hongbing Wang

    Southeast University, China

    6 publications · 234 citations

    6

    h-index

  2. QY
    Qi Yu

    Rochester Institute of Technology, United States

    6 publications · 235 citations

    6

    h-index

  3. AB
    Athman Bouguettaya

    The University of Sydney, Australia

    3 publications · 108 citations

    3

    h-index

  4. XH
    Xingguo Hu

    Southeast University, China

    3 publications · 157 citations

    3

    h-index

  5. AC
    Arshia Cont

    Institut de Recherche et Coordination Acoustique Musique, France

    2 publications · 130 citations

    2

    h-index

1–5 of 79

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 31 citations

    2

    h-index

  2. Knowledge-Based Systems

    2 publications · 100 citations

    2

    h-index

  3. Information Sciences

    1 publications · 72 citations

    1

    h-index

  4. Philosophy of Science

    1 publications · 20 citations

    1

    h-index

  5. Future Generation Computer Systems

    FGCS · 1 publications · 39 citations

    1

    h-index

1–5