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

TL;DR

ShuttleEnv is an interactive, data-driven badminton simulation environment that enables reinforcement learning research and strategic analysis by modeling rally dynamics based on elite-player data, without physics simulation.

Contribution

It introduces ShuttleEnv, a novel simulation platform grounded in real match data, facilitating realistic, interpretable agent interactions and strategic exploration in badminton AI research.

Findings

Demonstrated multiple trained agents within ShuttleEnv

Provided live visualization of badminton rallies

Enabled analysis of emergent strategies and decision-making behaviors

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