论文标题
一种增强学习羽毛球环境，用于模拟玩家策略（学生摘要）
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)
论文作者
论文摘要
精确分析运动的最新技术刺激了各种方法，以提高球员性能和粉丝的参与度。但是，现有方法只能评估离线性能，因为实时比赛中的测试需要详尽的成本，并且不能复制。为了在安全且可重复的模拟器中进行测试，我们专注于基于回合的运动，并通过模拟各个角度和设计各州，行动和培训程序的集会来引入羽毛球环境。这不仅使教练和球员模拟过去的比赛进行战术调查，还从迅速评估其新颖算法的研究人员中受益。
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.
