mlsanityData to 14 Oct 2026

Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning

arXiv 2606.06353 ↗First posted 4 Jun 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 32 references that are in the map. First posted on arXiv 4 Jun 2026.

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12 of 32 references.

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  1. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective9 Apr

Other references · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Graph Attention Networkscited by 8,923
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2021How Attentive are Graph Attention Networks?cited by 835
  5. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  6. 2018Actions Speak Louder Than Goals: Valuing Player Actions in Soccercited by 90
  7. 2024Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Taskscited by 45
  8. 2023TacticAI: an AI assistant for football tacticscited by 44
  9. 2020A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessionscited by 24
  10. 2023Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learningcited by 10
  11. 2026A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Footballcited by 1

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  1. 2025Interpretable Low-Dimensional Modeling of Spatiotemporal Agent States for Decision Making in Football Tactics
  2. 2026TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics
  3. 2021Towards optimized actions in critical situations of soccer games with deep reinforcement learning
  4. 2026Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football
  5. 2025Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach
  6. 2026Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football
  7. 2021From Motor Control to Team Play in Simulated Humanoid Football
  8. 2025TacEleven: generative tactic discovery for football open play