Personalized skill transfer optimization in swimming training through multi-agent reinforcement learning driven digital twin environments

Traditional swimming training methodologies face inherent limitations in providing personalized, adaptive, and scalable training solutions that accommodate diverse learning patterns and individual athlete characteristics. This research introduces a novel framework integrating multi-agent reinforcement learning with digital twin technology to create an intelligent swimming training environment capable of delivering personalized skill transfer optimization through meta-learning strategies. The proposed system addresses conventional training limitations by providing adaptive, data-driven training recommendations that evolve based on individual swimmer characteristics and performance dynamics. The multi-agent architecture enables simulation of complex training scenarios while incorporating real-time feedback mechanisms that continuously refine training strategies. Key contributions include: (1) development of a comprehensive digital twin swimming environment modeling biomechanical and hydrodynamic processes, (2) implementation of multi-agent reinforcement learning algorithms for personalized sports training, (3) integration of meta-learning based skill transfer optimization enabling efficient knowledge transfer across swimmers and contexts, and (4) experimental validation demonstrating improved training efficiency and performance outcomes. Experimental results show 34% faster convergence rates and 22% higher final performance scores compared to baseline methods, with 2.7× faster skill acquisition rates and 89% retention rates over extended periods. The framework demonstrates robust adaptation capabilities across diverse swimmer populations while maintaining computational efficiency and system stability.