Meta-Hierarchical Policy Gradients: Adaptive Meta-Learning for Personalized Music Skill Progression

We propose Meta-Hierarchical Policy Gradients (MHPG), a novel adaptive meta-learning framework designed to optimize personalized music skill progression by dynamically decomposing and weighting sub-skills. Conventional methods frequently address skill development as a unified process, while MHPG distinctly structures the interaction of essential musical elements—rhythm, pitch, dynamics, and articulation—via a layered incentive framework. The system functions by means of two synchronized levels: concurrent sub-policies for distinct sub-skills and a meta-learner adaptively adjusting their inputs according to immediate outcomes. The meta-learner applies a GRU-based encoder to analyze learner trajectories, producing dynamic weights that adjust emphasis among sub-skills, thereby creating customized learning routes. Moreover, the system integrates a simplified MAML-inspired optimization strategy, where meta-parameters are updated to minimize expected loss across diverse music learning tasks while accounting for hierarchical rewards. The proposed method interacts with standard modules by means of state depiction, policy implementation, and reward modification, which guarantees compatibility with current music education tools. Experiments show that MHPG effectively identifies intricate, personalized learning behaviors and achieves better results than uniform methods in terms of flexibility and processing speed. This study contributes to personalized education by introducing a structured, hierarchical model for skill development, directly applicable in music instruction and extending to adaptive educational technologies.