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Designing Deep Reinforcement Learning for Human Parameter Exploration

Hugo Scurto 1 Bavo van Kerrebroeck 1 Baptiste Caramiaux 2 Frédéric Bevilacqua 1
1 Interaction Son Musique Mouvement
STMS - Sciences et Technologies de la Musique et du Son
2 EX-SITU - Extreme Situated Interaction
Inria Saclay - Ile de France, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, IaH - Interaction avec l'Humain
Abstract : Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this paper, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users for sound design. We describe a series of user-centred studies to probe the creative benefits of these agents and adapting their design to exploration. Preliminary studies observing users' exploration strategies with parametric interfaces and testing different agent exploration behaviours led to the design of a fully-functioning prototype, called Co-Explorer, that we evaluated in a workshop with professional sound designers. We found that the Co-Explorer enables a novel creative workflow centred on human-machine partnership, which has been positively received by practitioners. We also highlight varied user exploration behaviors throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications.
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https://hal.archives-ouvertes.fr/hal-03124053
Contributor : Hugo Scurto <>
Submitted on : Thursday, January 28, 2021 - 1:10:21 PM
Last modification on : Tuesday, July 13, 2021 - 2:17:13 PM
Long-term archiving on: : Thursday, April 29, 2021 - 6:47:02 PM

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Hugo Scurto, Bavo van Kerrebroeck, Baptiste Caramiaux, Frédéric Bevilacqua. Designing Deep Reinforcement Learning for Human Parameter Exploration. ACM Transactions on Computer-Human Interaction, Association for Computing Machinery, 2021, 28 (1), pp.1-35. ⟨10.1145/3414472⟩. ⟨hal-03124053⟩

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