ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling
This provides a reusable platform for sports AI research, focusing on badminton strategy, but it is incremental as it applies existing methods to a new domain.
The authors tackled the problem of modeling badminton strategy by developing ShuttleEnv, a data-driven simulation environment for reinforcement learning, which uses elite-player match data and probabilistic models to simulate rally-level dynamics and enable interactive analysis of agent strategies.
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