Article: Application of deep reinforcement learning in sports competitive decision-making Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.5 pp.61 - 79 Abstract: Demand for tactical optimisation and decision support in sports competitions is growing; traditional rule-based methods suffer from poor adaptability and latency. This study builds a DRL-based decision model for Wushu Sanda, trained on multi-source data and validated in simulation. The agent learns policies via interaction to optimise tactical choices in dynamic contexts. Compared with rule-based and classical RL baselines (Q-learning, SARSA), our model achieves higher decision accuracy, larger cumulative reward, and faster convergence. It adapts to diverse scenarios and supports real-time tactical adjustment. We also identify challenges in data quality, computational cost, and cross-sport generalisation. The findings highlight DRL's practicality for competitive decision-making and outline directions for improving interpretability, sample efficiency, and deployment in live matches. Inderscience Publishers - linking academia, business and industry through research

Title: Application of deep reinforcement learning in sports competitive decision-making

Authors: Zijing Wang; Zhijun Li

Addresses: Guangxi Technology and Business Vocational College, Nanning, 530000, Guangxi, China ' Guangxi College for Preschool Education, Nanning, 530000, Guangxi, China

Abstract: Demand for tactical optimisation and decision support in sports competitions is growing; traditional rule-based methods suffer from poor adaptability and latency. This study builds a DRL-based decision model for Wushu Sanda, trained on multi-source data and validated in simulation. The agent learns policies via interaction to optimise tactical choices in dynamic contexts. Compared with rule-based and classical RL baselines (Q-learning, SARSA), our model achieves higher decision accuracy, larger cumulative reward, and faster convergence. It adapts to diverse scenarios and supports real-time tactical adjustment. We also identify challenges in data quality, computational cost, and cross-sport generalisation. The findings highlight DRL's practicality for competitive decision-making and outline directions for improving interpretability, sample efficiency, and deployment in live matches.

Keywords: deep reinforcement learning; DRL; sports competition; decision optimisation.

DOI: 10.1504/IJICT.2026.151532

International Journal of Information and Communication Technology, 2026 Vol.27 No.5, pp.61 - 79

Received: 19 Sep 2025
Accepted: 14 Oct 2025
Published online: 04 Feb 2026 *

Title: Application of deep reinforcement learning in sports competitive decision-making

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