人間参加型強化学習による音楽生成
Music Generation using Human-In-The-Loop Reinforcement Learning
人間のフィードバックを取り入れた強化学習（HITL RL）と音楽理論を組み合わせ、ユーザーの好みを報酬として楽曲をリアルタイム生成する枠組みを提案した。
著者: Aju Ani Justus
分類: cs.SD, cs.AI, cs.HC, cs.LG, eess.AS
原文アブストラクト
This paper presents an approach that combines Human-In-The-Loop Reinforcement Learning (HITL RL) with principles derived from music theory to facilitate real-time generation of musical compositions. HITL RL, previously employed in diverse applications such as modelling humanoid robot mechanics and enhancing language models, harnesses human feedback to refine the training process. In this study, we develop a HILT RL framework that can leverage the constraints and principles in music theory. In particular, we propose an episodic tabular Q-learning algorithm with an epsilon-greedy exploration policy. The system generates musical tracks (compositions), continuously enhancing its quality through iterative human-in-the-loop feedback. The reward function for this process is the subjective musical taste of the user.