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

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

資訊工程學系

研究成果: Conference contribution › 同行評審

3 引文斯高帕斯（Scopus）

摘要

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.

原文	English
主出版物標題	AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations
編輯	Brian Williams, Yiling Chen, Jennifer Neville
發行者	AAAI press
頁面	16232-16233
頁數	2
ISBN（電子）	9781577358800
出版狀態	Published - 27 6月 2023
事件	37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, 美國持續時間: 7 2月 2023 → 14 2月 2023

出版系列

名字	Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
卷	37

Conference

Conference	37th AAAI Conference on Artificial Intelligence, AAAI 2023
國家/地區	美國
城市	Washington
期間	7/02/23 → 14/02/23

其它文件與鏈接

Link to publication in Scopus

引用此

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. 於 B. Williams, Y. Chen, & J. Neville (編輯), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations (頁 16232-16233). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; 卷 37). AAAI press.

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

@inproceedings{465b306ef6d14dc0b07221c389d3f483,

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",

language = "English",

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

publisher = "AAAI press",

pages = "16232--16233",

editor = "Brian Williams and Yiling Chen and Jennifer Neville",

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. 於 B Williams, Y Chen & J Neville (編輯), AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, 卷 37, AAAI press, 頁 16232-16233, 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, 美國, 7/02/23.

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

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - A Reinforcement Learning Badminton Environment for Simulating Player Tactics

AU - Huang, Li Chun

AU - Hseuh, Nai Zen

AU - Chien, Yen Che

AU - Wang, Wei Yao

AU - Wang, Kuang Da

AU - Peng, Wen Chih

PY - 2023/6/27

Y1 - 2023/6/27

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85168248293&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85168248293

T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023

SP - 16232

EP - 16233

BT - AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

A2 - Williams, Brian

A2 - Chen, Yiling

A2 - Neville, Jennifer

PB - AAAI press

T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023

Y2 - 7 February 2023 through 14 February 2023

ER -

Huang LC, Hseuh NZ, Chien YC, Wang WY, Wang KD, Peng WC. A Reinforcement Learning Badminton Environment for Simulating Player Tactics. 於 Williams B, Chen Y, Neville J, 編輯, 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).

A Reinforcement Learning Badminton Environment for Simulating Player Tactics

摘要

出版系列

Conference

其它文件與鏈接

指紋

引用此