ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling

Abstract

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

Figures (2)

• Figure 1: The workflow of our data-driven badminton environment. The RL agent interacts with the environment in a standard loop. Inside the environment, the outcome of an action $a_t$ is determined by a two-stage probabilistic process, driven by two pre-trained models ($M_{\text{succ}}$ and $M_{\text{ret}}$) which leverage the elite-level badminton match data. The agent receives a new observation $o_{t+1}$ generated from the action history window to make the next decision and a sparse reward $R_t$ at the end of a rally.
• Figure 2: Representative frame from the visualization demo. The rendering overlays rally context (active player, action metadata, and score) on a 3D court scene. The visualization highlights how different shot selections and target areas lead to distinct rally trajectories and outcomes, supporting qualitative inspection of tactical decision-making.