Dynamic matching strategy for college students’ innovative training projects based on reinforcement learning optimization

Abstract Matching college students with appropriate innovative training projects is a challenging task that often relies on static assignment techniques, which overlook individual interests, skills, and learning styles. Traditional methods lead to mismatched assignments, decreased engagement, and lower innovation output. This research presents an intelligent, dynamic matching algorithm that maximizes the allocation of students to innovative training projects, utilizing techniques based on reinforcement learning (RL) to analyze interactions and continually optimize assignment decisions. The data was collected, which involves the profiles of students, their interests and skills rating, and project metadata. The data is pre-processed to normalize and encode categorical features. The extraction of features, dimensionality reduction, and significant matching signs are obtained by the Principal Component Analysis (PCA). The Q-Learning Algorithm Tuned Dueling Deep Q-Network (QLA-D2QNet) was developed to dynamically learn optimal matching policies through interaction with the environment and reward feedback. QLA is used to learn optimal assignment strategies by trial and error. D2QNet separates value and advantage functions to enhance policy learning stability. The model constantly adjusts matching policies based on feedback for project success and student satisfaction. The experimental results indicate that the QLA-D2QNet significantly outperforms traditional manual approaches. The best results include a Skill-Interest Fit of 89.10%, a Project Completion Rate of 94.00%, and a Skill Improvement Score of 31.40%. The suggested QLA-D2QNet model provides a scalable, flexible, and successful technique for dynamically matching students to training projects, resulting in dramatically improved educational outcomes in creative learning environments.