Dynamically planning in multi-agent systems has been explored to improve decision-making in various domains. Professional basketball serves as a compelling example of a dynamic spatio-temporal game, encompassing both concealed strategic policies and decision-making. However, processing the diverse on-court signals and navigating the vast space of potential actions and outcomes makes it difficult for existing approaches to swiftly identify optimal strategies in response to evolving circumstances. In this study, we first formulate the sequential decision-making process as a conditional trajectory generation process. We further introduce PLAYBEST (PLAYer BEhavior SynThesis), a method for enhancing player decision-making. We extend the state-of-the-art generative model, diffusion probabilistic model, to learn challenging multi-agent environmental dynamics from historical National Basketball Association (NBA) player motion tracking data. To incorporate data-driven strategies, an auxiliary value function is trained using the play-by-play data with corresponding rewards acting as the plan guidance. To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling. We validate the effectiveness of PLAYBEST via comprehensive simulation studies from real-world data, contrasting the generated trajectories and play strategies with those employed by professional basketball teams. Our results reveal that the model excels at generating high-quality basketball trajectories that yield efficient plays, surpassing conventional planning techniques in terms of adaptability, flexibility, and overall performance. Moreover, the synthesized play strategies exhibit a remarkable alignment with professional tactics, highlighting the model's capacity to capture the intricate dynamics of basketball games.

翻译：在多智能体系统中进行动态规划已被探索用于改进各领域的决策制定。职业篮球作为动态时空博弈的典型范例，涵盖了隐藏策略和决策过程。然而，处理多样的场内信号并探索潜在动作与结果的广阔空间，使得现有方法难以快速识别应对不断变化局面的最优策略。本研究首先将序贯决策过程表述为条件轨迹生成过程，并进一步引入PLAYBEST（PLAYer BEhavior SynThesis）方法以增强球员决策能力。我们将最先进的生成模型——扩散概率模型——进行扩展，使之能够从历史美国国家篮球协会（NBA）球员运动追踪数据中学习复杂多智能体环境动态。为了融合数据驱动策略，我们利用逐回合数据训练辅助价值函数，并以对应奖励作为计划引导。为实现奖励引导的轨迹生成，引入条件采样机制，将扩散模型以价值函数为条件进行约束，并执行分类器引导采样。通过基于真实数据的全面仿真研究，将生成轨迹和比赛策略与职业篮球队采用的策略进行对比，验证了PLAYBEST的有效性。结果表明，该模型在生成高质量篮球轨迹以产生高效回合方面表现卓越，在适应性、灵活性和整体性能上均超越传统规划技术。此外，合成比赛策略与专业战术高度吻合，凸显了模型捕捉篮球比赛复杂动态的能力。