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 article, 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 behaviours throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications.

中文翻译：

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为人体参数探索设计深度强化学习

用于生成数字声音的软件工具通常会为用户提供高维参数化界面，这可能不利于探索各种声音设计。在本文中，我们建议研究使用深度强化学习的人工代理，以与用户合作探索参数空间以进行声音设计。我们描述了一系列以用户为中心的研究，以探索这些代理的创造性优势并使其设计适应探索。初步研究通过参数界面观察用户的探索策略并测试不同的代理探索行为，最终设计出一个功能齐全的原型，称为 Co-Explorer，我们在与专业声音设计师的研讨会上对其进行了评估。我们发现 Co-Explorer 实现了以人机合作为中心的新颖创意工作流程，受到了从业者的好评。我们还强调了在与我们的系统合作期间的各种用户探索行为。最后，我们制定了在创意数字应用程序中实现这种共同探索工作流程的设计指南。