用图强化学习优化角球战术，发现更高效的球员站位策略。
Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning
- 基于图结构的强化学习，动态调整球员位置与速度
- 在3000+英超角球上提升首次触球射门概率
- 适合足球战术研究者与数据驱动教练团队
机器学习正被广泛用于评估足球战术，但现有方法多聚焦于历史动作分析或分析师设定的反事实场景。本文突破传统模仿历史模式的局限，致力于发现可泛化的球员配置与策略。聚焦角球进攻，提出一个决策问题：由中央策略动态调整进攻球员的位置和速度，以最大化首次触球射门概率。不同于经典优化中孤立求解特定阵型，本文构建了一种基于图结构数据的强化学习架构，可生成适用于任意初始站位的通用调整策略。在超过3000个英超角球数据上验证，该方法在相同推理预算下显著优于基线优化技术。结果表明，图强化学习能推动定位球分析从历史评估转向奖励驱动的战术发现。
原文摘要 · Abstract (English)
Machine learning is increasingly employed for the evaluation of football tactics. However, existing approaches focus on characterising historical actions or analyst-specified counterfactual scenarios. In this work, we seek to go beyond the imitation of historically observed patterns towards discovering new generalisable player configurations and strategies. To tackle this, we focus on optimising corner kick routines, and formulate a decision-making problem in which a central policy makes adjustments to attacking player positions and velocities to maximise first contact shot probability. Unlike classic optimisation that solves for isolated setups, we contribute a reinforcement learning architecture operating on graph-structured data that yields a general policy for adjusting arbitrary starting player positions. Evaluated on over 3,000 Premier League corners, our approach strongly outperforms baseline optimisation techniques under matched inference budgets. Our results suggest that graph reinforcement learning can shift set-piece analysis from historical evaluation and imitation towards reward-driven tactical discovery.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。