用真实羽毛球比赛数据构建可交互的强化学习环境，模拟对战策略。
ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling
- 基于顶尖选手比赛数据建模，用概率方法模拟回合动态。
- 支持训练多智能体并实时可视化对战过程与决策行为。
- 适合体育人工智能、策略分析与交互式演示研究者使用。
我们提出ShuttleEnv，一个基于真实精英选手比赛数据的交互式、数据驱动的羽毛球仿真环境，旨在支持快速对抗性运动中的强化学习与策略行为分析。该环境采用显式的概率模型模拟回合级动态，实现真实且可解释的智能体-对手交互，无需依赖物理引擎。在演示中，我们展示了多个在ShuttleEnv中训练的智能体，并提供实时、分步的羽毛球对战可视化，使观众能够探索不同打法、观察策略涌现，并交互式分析决策行为。ShuttleEnv可作为体育人工智能研究、可视化及演示的通用平台。演示视频链接：https://drive.google.com/file/d/1hTR4P16U27H2O0-w316bR73pxE2ucczX/view
原文摘要 · Abstract (English)
We present ShuttleEnv, an interactive and data-driven simulation environment for badminton, designed to support reinforcement learning and strategic behavior analysis in fast-paced adversarial sports. The environment is grounded in elite-player match data and employs explicit probabilistic models to simulate rally-level dynamics, enabling realistic and interpretable agent-opponent interactions without relying on physics-based simulation. In this demonstration, we showcase multiple trained agents within ShuttleEnv and provide live, step-by-step visualization of badminton rallies, allowing attendees to explore different play styles, observe emergent strategies, and interactively analyze decision-making behaviors. ShuttleEnv serves as a reusable platform for research, visualization, and demonstration of intelligent agents in sports AI. Our ShuttleEnv demo video URL: https://drive.google.com/file/d/1hTR4P16U27H2O0-w316bR73pxE2ucczX/view
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