Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms

Abstract

Tactical intelligence refers to the ability to gather, analyze, and apply information swiftly and effectively to achieve specific goals or objectives in dynamic and often challenging situations. It involves understanding the current environment, assessing available resources, and making informed decisions to gain a competitive advantage or mitigate risks. Tactical Intelligent Decision Modelling in Sports Competitions, grounded in Reinforcement Learning (RL) algorithms, represents a cutting-edge approach to enhancing strategic decision-making processes within athletic domains. This innovative methodology leverages RL's ability to learn optimal actions through trial and error, adapting strategies based on feedback received from the environment. By applying RL algorithms to sports competitions, teams can develop intelligent decision models that dynamically adjust tactics and strategies in response to changing game conditions and opponent behaviors. Such models enable teams to optimize their performance, exploit opponent weaknesses, and capitalize on opportunities during competitions. This paper introduces Predictive Weighted Big Data Reinforcement Learning (PWBDRL), an innovative approach aimed at optimizing decision-making processes and performance outcomes in sports competitions. Leveraging predictive analytics, big data techniques, and reinforcement learning, PWBDRL offers a comprehensive framework for athlete prediction and strategy optimization. The results show a remarkable increase in win rates, with the RL-based decision models achieving an average win rate of 80.5% compared to 65.2% with traditional methods. Additionally, we observe a substantial enhancement in average scores, with the RL-based models achieving an average score of 95.6 compared to 78.3 with baseline approaches. Moreover, the RL-based models exhibit superior adaptability, requiring fewer iterations to converge to optimal strategies, with an average convergence time of 200 episodes compared to 500 episodes for traditional methods.