Optimizing Competitive Sports Training Strategies with Adaptive Deep Reinforcement Learning

To address the limitations of traditional rulebased and static approaches, this study presents an innovative deep reinforcement learning (DRL) framework designed to improve training strategies in competitive sports. Describing the training process as a Markov Decision Process (MDP), the framework creates personalized and customizable training plans by integrating high-dimensional physiological data and dynamic environmental factors. Using an advanced neural network architecture and an iterative learning process, the DRL agent optimizes training intensity, workload distribution, and recovery plans in real time to improve performance and reduce injury. Numerous simulations demonstrate the effectiveness of the framework. Compared to traditional methods, the DRL agent improved endurance metrics by 50% and reduced training time by $30 \%$. The agent was able to quickly adapt to changing environments, such as sudden changes in workload or fatigue, and remained stable across situations. In addition, the state-action mapping demonstrates the agent’s ability to dynamically adjust its strategy when its physiological state changes. It also highlights the agent’s realtime decision-making capabilities. This research has made significant progress in data-driven personalized exercise training methods. These methods optimize exercise performance in complex, high-dimensional environments.