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
Parents
29
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
Debang Li, Huikai Wu, Junge Zhang et al. · 2018 · 134 citations
Omax brothers : a dynamic topology of agents for improvization learning
Gérard Assayag, Georges Bloch, Marc Chemillier et al. · 2006 · 69 citations · Full text
1–5 of 29
79
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Institut de Recherche et Coordination Acoustique Musique, France
2 publications · 130 citations
2
h-index
1–5 of 79
34

Southeast University
Nanjing, China
9 h-index
38 publications
1.6K citations
Rochester Institute of Technology
Rochester, United States
5 h-index
5 publications
218 citations

Université de Caen Normandie
Caen, France
2 h-index
2 publications
130 citations
University of California San Diego
San Diego, United States
2 h-index
2 publications
130 citations

The University of Sydney
Sydney, Australia
2 h-index
3 publications
103 citations
Université de Strasbourg
Strasbourg, France
2 h-index
2 publications
130 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
2 h-index
2 publications
79 citations
Indian Institute of Technology Dhanbad
Dhanbad, India
1 h-index
2 publications
28 citations
1–8 of 34
Repository · 2 publications · 31 citations
2
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2 publications · 100 citations
2
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1 publications · 72 citations
1
h-index
1 publications · 20 citations
1
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FGCS · 1 publications · 39 citations
1
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1–5