ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 18 Mar 2026.
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7 of 10 references.
References · 7
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
- 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 257
- 2021ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badmintoncited by 21
- 2022Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badmintoncited by 10
- 2023Benchmarking Stroke Forecasting with Stroke-Level Badminton Datasetcited by 8
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Citing papers per year: 2026: 1.
1 paper.