ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915871373393920 |
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| author | Li, Ang Gong, Xinyang Chen, Bozhou Lu, Yunlong Ji, Jiaming Wang, Yongyi Yang, Yaodong Li, Wenxin |
| author_facet | Li, Ang Gong, Xinyang Chen, Bozhou Lu, Yunlong Ji, Jiaming Wang, Yongyi Yang, Yaodong Li, Wenxin |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17324 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling Li, Ang Gong, Xinyang Chen, Bozhou Lu, Yunlong Ji, Jiaming Wang, Yongyi Yang, Yaodong Li, Wenxin Artificial Intelligence Machine Learning 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 |
| title | ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.17324 |