We present the CoachAI Badminton Environment, a reinforcement learning (RL) environment tailored for AI-driven sports analytics. In contrast to traditional environments using rule-based opponents or simplistic physics-based randomness, our environment integrates authentic opponent AIs and realistic randomness derived from real-world matches data to bridge the performance gap encountered in real-game deployments. This novel feature enables RL agents to seamlessly adapt to genuine scenarios. The CoachAI Badminton Environment empowers researchers to validate strategies in intricate real-world settings, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities. By bridging the RL environment with actual badminton games, our environment is able to advance the discovery of winning strategies for players. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environment.

The growing demand for precise sports analysis has been explored to improve athlete performance in various sports (e.g., basketball, soccer). However, existing methods for different sports face challenges in validating strategies in environments due to simple rule-based opponents leading to performance gaps when deployed in real-world matches. In this paper, we propose the CoachAI Badminton Environment, a novel reinforcement learning (RL) environment with realistic opponents for badminton, which serves as a compelling example of a turn-based game. It supports researchers in exploring various RL algorithms with the badminton context by integrating state-of-the-art tactical-forecasting models and real badminton game records. The Badminton Benchmarks are proposed with multiple widely adopted RL algorithms to benchmark the performance of simulating matches against real players. To advance novel algorithms and developments in badminton analytics, we make our environment open-source, enabling researchers to simulate more complex badminton sports scenarios based on this foundation. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI%20Badminton%20Environment.