强化学习爵士乐即兴演奏：当音乐遇上博弈论
Reinforcement Learning Jazz Improvisation: When Music Meets Game Theory
AI总结：
本研究构建爵士乐即兴演奏的博弈论模型，通过强化学习对比多种随机即兴策略的配对表现，发现和弦跟随强化学习策略收益最优，为音乐AI及策略优化提供了新框架。
摘要：
音乐现场表演始终魅力十足，音乐家之间的动态互动以及与观众的互动让即兴演奏充满不可预测性。爵士乐即兴演奏是尤其值得从理论层面深入研究的典型案例。本文提出了一种全新的爵士乐即兴演奏数学博弈论模型，为研究音乐理论与即兴创作方法提供了框架。我们采用以强化学习为主的计算建模方法，探索多种随机即兴演奏策略及其配对组合在即兴演奏中的表现。研究发现，效果最佳的策略对是：对近期收益做出反应的策略（逐步变化策略，Stepwise Changes），与仅选用给定和弦内音符的强化学习策略（和弦跟随强化学习，Chord-Following Reinforcement Learning）的组合。与之相反，对搭档上一个音符做出反应并尝试与之和声的策略（和声预测策略，Harmony Prediction）的策略对，产生了非对照组中最低的收益和最高的标准差，这表明基于对搭档演奏的即时反应选择音符会导致结果不稳定。平均而言，和弦跟随强化学习策略的平均收益最高，而和声预测策略的平均收益最低。本研究为爵士乐之外的广阔应用奠定了基础：包括利用人工智能（AI）模型从音频片段中提取数据以优化音乐奖励系统，以及基于现有爵士独奏训练机器学习（ML）模型以进一步优化博弈中的策略。
英文摘要：
Live performances of music are always charming, with the unpredictability of improvisation due to the dynamic between musicians and interactions with the audience. Jazz improvisation is a particularly noteworthy example for further investigation from a theoretical perspective. Here, we introduce a novel mathematical game theory model for jazz improvisation, providing a framework for studying music theory and improvisational methodologies. We use computational modeling, mainly reinforcement learning, to explore diverse stochastic improvisational strategies and their paired performance on improvisation. We find that the most effective strategy pair is a strategy that reacts to the most recent payoff (Stepwise Changes) with a reinforcement learning strategy limited to notes in the given chord (Chord-Following Reinforcement Learning). Conversely, a strategy that reacts to the partner's last note and attempts to harmonize with it (Harmony Prediction) strategy pair yields the lowest non-control payoff and highest standard deviation, indicating that picking notes based on immediate reactions to the partner player can yield inconsistent outcomes. On average, the Chord-Following Reinforcement Learning strategy demonstrates the highest mean payoff, while Harmony Prediction exhibits the lowest. Our work lays the foundation for promising applications beyond jazz: including the use of artificial intelligence (AI) models to extract data from audio clips to refine musical reward systems, and training machine learning (ML) models on existing jazz solos to further refine strategies within the game.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
