Research and Practice on Intelligent Optimization Model of Table Tennis Tactics Based on Reinforcement Learning Algorithm

In competitive sports, it is crucial for athletes to demonstrate their technical level and improve their athletic performance through scientific and appropriate training methods and flexible and superb competition skills. Especially in the highly strategic sport of table tennis, the quality of tactics often determines the outcome of the game. However, traditional table tennis tactical analysis and optimization often rely on the personal experience of coaches and the intuition of athletes, lacking scientific and systematic approaches. To address this issue, this paper proposes an intelligent optimization model for table tennis tactics based on reinforcement learning (RL) algorithm. This model simulates the competition environment, allowing intelligent agents to conduct a large amount of trial-and-error learning in the virtual environment, to find the optimal tactical strategy. The experimental results show that the model can significantly improve the performance of athletes in matches, including key indicators such as win rate, loss rate, and control of ball weight time. This study not only provides scientific tactical analysis and optimization methods for table tennis players and coaches, but also demonstrates the broad application prospects of artificial intelligence (AI) technology in the field of sports.