Polyphonic music melody generation model based on inverse reinforcement learning algorithm

Automated polyphonic music generation remains a challenging task due to the difficulty in designing reward functions and modelling long-range dependencies.To address this, this paper proposes a polyphonic music generation model based on generative adversarial imitation learning.Our model employs a Gated Transformer-XL as its core to effectively capture intricate contrapuntal relationships.Experimental results demonstrate that the model achieves superior performance across multiple metrics: it reduces the Earth mover's distance to 0.23; increases voice separation mutual information to 0.45, and achieves a 91.2% harmonic rule compliance rate.In subjective evaluations, the model attained average opinion scores of 8.7 for melodic fluency and 8.9 for harmonic richness, significantly outperforming all baseline models.These results validate the effectiveness of our approach in generating high-quality polyphonic music with both technical proficiency and artistic merit.