1 research outputs found
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)
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.Comment: Accepted by AAAI 2023 Student Abstract, code is available at
https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environmen