Intelligent Tactical Decision System for Tennis Using Deep Deterministic Policy Gradient Algorithm

This paper constructs a tennis intelligent tactical decision system through the Deep Deterministic Policy Gradient (DDPG) algorithm. It solves the problem that traditional tactical decision-making relies on experience, uses DDPG to analyze real-time game data and dynamically generates the optimal tactical plan. This paper defines the key state variables that affect game tactical decisions, including player and opponent positions, batting parameters, current game score situation and game duration. The policy network and value network can be initialized, and training data can be obtained by interacting with the environment and optimized using the experience replay pool. The policy network is optimized using mini-batch gradient descent and policy gradient. Regarding experimental settings, the system conducted simulation tests on 8 tennis opponents with different styles, including attacking, defensive, all-around, topspin, backhand, speed, tactical and powerful and evaluated multiple key performance indicators such as batting success rate, scoring rate, decision time and entropy of strategy distribution. The experimental results show that the average batting success rate and average scoring rate of the DDPG algorithm reached 89.1% and 81.4%, respectively. The decision time of the DDPG system is between 11 ms and 16 ms, which is better than the other three systems. The DDPG system performs outstandingly in terms of strategy diversity, and the average entropy value of strategy distribution reaches 2.89, which reflects a strong ability to change tactics and avoid a single strategy. The tennis intelligent tactical decision system constructed in this paper can effectively improve tactical performance in tennis games and provide a feasible solution for sports intelligent systems.