Optimization of Wushu Movement Learning and Training Methods Based on Deep Reinforcement Learning
* by Guohai Cheng * International Journal of e-Collaboration, September 2026 *
Abstract
The present research introduces WushuDRL-Net, an intelligent framework based on deep reinforcement learning (DRL) to enhance Wushu movement learning and training. The system employs the Proximal Policy Optimization with Motion Alignment Feedback (PPO-MAF) technique, which combines expert imitation, rhythm alignment, and scoring accuracy, to give real-time motion evaluation and correction. Using competition-grade datasets, WushuDRL-Net aligned expert performances with over 90% accuracy and increased execution consistency by 17%. The technology offers dynamic feedback, rhythm synchronization, and adaptive skill enhancement for routines such as Changquan, Nanquan, and Taolu. The experimental results reveal significant increases (p < 0.01) in rhythm control and form precision, demonstrating the scalability and efficiency of WushuDRL-Net. By bridging the gap between traditional martial arts instruction and state-of-the-art AI-driven training, this study encourages intelligent, data-driven Wushu practice.
About the author(s)
Guohai Cheng is currently a Sixth-Duan Instructor of Chinese martial arts a first-class Judge of martial arts routines, and a Certified Martial Arts Duan Examiner. He is teaching with Changzhou Institute of Technology, Changzhou, Jiangsu, with a research focus on the scientific training of martial arts. In December 2024, he was awarded the title of National Outstanding Judge of Traditional Martial Arts. In 2025, he received the honour of Outstanding Judge of Martial Arts in National Martial Arts Hometowns.
