Enhancing Badminton Player Performance via a Closed-Loop AI Approach: Imitation, Simulation, Optimization, and Execution

Abstract

In recent years, the sports industry has witnessed a significant rise in interest in leveraging artificial intelligence to enhance players' performance. However, the application of deep learning to improve badminton athletes' performance faces challenges related to identifying weaknesses, generating winning suggestions, and validating strategy effectiveness. These challenges arise due to the limited availability of realistic environments and agents. This paper aims to address these research gaps and make contributions to the badminton community. To achieve this goal, we propose a closed-loop approach consisting of six key components: Badminton Data Acquisition, Imitating Players' Styles, Simulating Matches, Optimizing Strategies, Training Execution, and Real-World Competitions. Specifically, we developed a novel model called RallyNet, which excels at imitating players' styles, allowing agents to accurately replicate real players' behavior. Secondly, we created a sophisticated badminton simulation environment that incorporates real-world physics, faithfully recreating game situations. Thirdly, we employed reinforcement learning techniques to improve players' strategies, enhancing their chances of winning while preserving their unique playing styles. By comparing strategy differences before and after improvement, we provide winning suggestions to players, which can be validated against diverse opponents within our carefully designed environment. Lastly, through collaborations with badminton venues and players, we apply the generated suggestions to the players' training and competitions, ensuring the effectiveness of our approach. Moreover, we continuously gather data from training and competitions, incorporating it into the closed-loop cycle to refine strategies and suggestions. This research presents an innovative approach for continuously improving players' performance, contributing to the field of AI-driven sports performance enhancement.