A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)
This provides a tool for coaches, players, and researchers to analyze tactics and evaluate algorithms in a simulated setting, but it is incremental as it adapts existing methods to a new domain.
The authors tackled the problem of real-time sports analysis by creating a badminton reinforcement learning environment that simulates rallies with various angles, enabling safe and reproducible testing of player tactics and algorithms.
Recent techniques for analyzing sports precisely has stimulated various approaches to improve player performance and fan engagement. However, existing approaches are only able to evaluate offline performance since testing in real-time matches requires exhaustive costs and cannot be replicated. To test in a safe and reproducible simulator, we focus on turn-based sports and introduce a badminton environment by simulating rallies with different angles of view and designing the states, actions, and training procedures. This benefits not only coaches and players by simulating past matches for tactic investigation, but also researchers from rapidly evaluating their novel algorithms.