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
@article{arxiv.2603.17324,
title = {ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling},
author = {Ang Li and Xinyang Gong and Bozhou Chen and Yunlong Lu and Jiaming Ji and Yongyi Wang and Yaodong Yang and Wenxin Li},
journal= {arXiv preprint arXiv:2603.17324},
year = {2026}
}