Football Tactical Scenario Generation and Multi-Strategy Confrontation Simulation Guided by Diffusion Models

Abstract

This paper proposes a collaborative framework integrating diffusion models and multi-agent reinforcement learning to address unrealistic trajectory generation and tactical incoherence in high-dimensional football tactical simulation. A conditional diffusion model is trained on 200 professional matches from Europe’s top five leagues during the 2021–2023 seasons to learn tactical distributions and generate spatiotemporal trajectories through denoising. Tactical labels and a graph attention mechanism are incorporated to improve structural rationality and semantic controllability. The generated trajectories are then used directly as the initial state of the simulation environment. Hierarchical action design and behavioral cloning pre-training initialize multi-agent policies so that decision-making begins from tactically plausible states. Finally, the multi-agent proximal policy optimization algorithm is applied in a red-blue confrontation environment, with ball possession, spatial utilization, and tactical consistency rewards used to support dynamic deduction. Experiments confirm high trajectory fidelity, with average absolute trajectory error of 0.82–1.03 m, tactical consistency up to 83.4% role-behavior fit, and dynamic evolution stability of 3.1 ×10⁻² strategy volatility. The framework provides a generative simulation method for multi-agent spatiotemporal systems and may inform scenario synthesis in wireless communication environments.