A Reinforcement Learning Badminton Environment for Simulating Player Tactics

Li Chun Huang; Nai Zen Hseuh; Yen Che Chien; Wei Yao Wang; Kuang Da Wang; Wen Chih Peng

doi:10.1609/aaai.v37i13.26976

A Reinforcement Learning Badminton Environment for Simulating Player Tactics

Li Chun Huang, Nai Zen Hseuh, Yen Che Chien, Wei Yao Wang, Kuang Da Wang, Wen Chih Peng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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. Our code is available at https://github.com/wywyWang/CoachAIProjects/tree/main/Strategic%20Environment.

Original language	English
Title of host publication	AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations
Editors	Brian Williams, Yiling Chen, Jennifer Neville
Publisher	AAAI press
Pages	16232-16233
Number of pages	2
ISBN (Electronic)	9781577358800
DOIs	https://doi.org/10.1609/aaai.v37i13.26976
State	Published - 27 Jun 2023
Event	37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023

Publication series

Name	Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume	37

Conference

Conference	37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/Territory	United States
City	Washington
Period	7/02/23 → 14/02/23

Access to Document

10.1609/aaai.v37i13.26976

Cite this

Huang, L. C., Hseuh, N. Z., Chien, Y. C., Wang, W. Y., Wang, K. D., & Peng, W. C. (2023). A Reinforcement Learning Badminton Environment for Simulating Player Tactics. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations (pp. 16232-16233). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). AAAI press. https://doi.org/10.1609/aaai.v37i13.26976

Huang, Li Chun ; Hseuh, Nai Zen ; Chien, Yen Che et al. / A Reinforcement Learning Badminton Environment for Simulating Player Tactics. AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. editor / Brian Williams ; Yiling Chen ; Jennifer Neville. AAAI press, 2023. pp. 16232-16233 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023).

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title = "A Reinforcement Learning Badminton Environment for Simulating Player Tactics",

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. Our code is available at https://github.com/wywyWang/CoachAIProjects/tree/main/Strategic%20Environment.",

author = "Huang, {Li Chun} and Hseuh, {Nai Zen} and Chien, {Yen Che} and Wang, {Wei Yao} and Wang, {Kuang Da} and Peng, {Wen Chih}",

note = "Publisher Copyright: Copyright {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",

year = "2023",

month = jun,

day = "27",

doi = "10.1609/aaai.v37i13.26976",

language = "English",

series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",

publisher = "AAAI press",

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booktitle = "AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations",

}

Huang, LC, Hseuh, NZ, Chien, YC, Wang, WY, Wang, KD & Peng, WC 2023, A Reinforcement Learning Badminton Environment for Simulating Player Tactics. in B Williams, Y Chen & J Neville (eds), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37, AAAI press, pp. 16232-16233, 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, United States, 7/02/23. https://doi.org/10.1609/aaai.v37i13.26976

A Reinforcement Learning Badminton Environment for Simulating Player Tactics. / Huang, Li Chun; Hseuh, Nai Zen; Chien, Yen Che et al.
AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. ed. / Brian Williams; Yiling Chen; Jennifer Neville. AAAI press, 2023. p. 16232-16233 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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. Our code is available at https://github.com/wywyWang/CoachAIProjects/tree/main/Strategic%20Environment.

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Huang LC, Hseuh NZ, Chien YC, Wang WY, Wang KD, Peng WC. A Reinforcement Learning Badminton Environment for Simulating Player Tactics. In Williams B, Chen Y, Neville J, editors, AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. AAAI press. 2023. p. 16232-16233. (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023). doi: 10.1609/aaai.v37i13.26976

A Reinforcement Learning Badminton Environment for Simulating Player Tactics

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