# Designing Deep Reinforcement Learning for Human Parameter Exploration

## TL;DR

This paper introduces Co-Explorer, a deep reinforcement learning-based tool that collaborates with sound designers to explore high-dimensional parameter spaces, fostering innovative sound design workflows.

## Contribution

It presents a novel human-AI partnership system for sound parameter exploration, including design guidelines and empirical evaluation with professional practitioners.

## Key findings

- Co-Explorer facilitates a new creative workflow for sound design.
- Users exhibit diverse exploration behaviors with the system.
- Practitioners positively received the collaborative exploration approach.

## 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.