In recent years, with the rapid development of artificial intelligence technology, the field of music education has begun to explore new teaching models. Traditional music education research methods have primarily focused on single-modal studies such as note recognition and instrument performance techniques, often overlooking the importance of multimodal data integration and interactive teaching. Existing methods often struggle with handling multimodal data effectively, unable to fully utilize visual, auditory, and textual information for comprehensive analysis, which limits the effectiveness of teaching. To address these challenges, this project introduces MusicARLtrans Net, a multimodal interactive music education agent system driven by reinforcement learning. The system integrates Speech-to-Text (STT) technology to achieve accurate transcription of user voice commands, utilizes the ALBEF (Align Before Fuse) model for aligning and integrating multimodal data, and applies reinforcement learning to optimize teaching strategies. This approach provides a personalized and real-time feedback interactive learning experience by effectively combining auditory, visual, and textual information. The system collects and annotates multimodal data related to music education, trains and integrates various modules, and ultimately delivers an efficient and intelligent music education agent. Experimental results demonstrate that MusicARLtrans Net significantly outperforms traditional methods, achieving an accuracy of 96.77% on the LibriSpeech dataset and 97.55% on the MS COCO dataset, with marked improvements in recall, F1 score, and AUC metrics. These results highlight the system's superiority in speech recognition accuracy, multimodal data understanding, and teaching strategy optimization, which together lead to enhanced learning outcomes and user satisfaction. The findings hold substantial academic and practical significance, demonstrating the potential of advanced AI-driven systems in revolutionizing music education.