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

## TL;DR

This paper introduces a new swimming training system using digital twins and AI to personalize and optimize skill learning for athletes.

## Contribution

A novel framework combining multi-agent reinforcement learning and digital twin environments for personalized swimming training.

## Key findings

- The system achieved 34% faster convergence rates compared to baseline methods.
- Swimmers showed 22% higher final performance scores and 2.7× faster skill acquisition.
- The framework maintained 89% skill retention over extended periods and adapted well to diverse populations.

## Abstract

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.