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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月13日

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  • Zhengliang Wu

摘要

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.