A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 6 references that are in the map. First posted on arXiv 22 Nov 2022.
Builds on
4 of 6 references.
References · 4
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
- 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
- 2021ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badmintoncited by 21
- 2021Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matchescited by 10
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Citing papers per year: 2023: 2.
2 papers.