Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model

doi:10.3390/s23249904

	Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model
	Yang, Yuanbo 1; Li, Feimo2 ; Chang, Hongxing2
发表期刊	SENSORS
	2023-12-01
卷号	23 期号:24 页码:12
通讯作者	Li, Feimo(lifeimo2012@ia.ac.cn)
摘要	This paper studies the tactical decision-making model of short track speed skating based on deep reinforcement learning, so as to improve the competitive performance of corresponding short track speed skaters. Short track speed skating, a traditional discipline in the Winter Olympics since its establishment in 1988, has consistently garnered attention. As artificial intelligence continues to advance, the utilization of deep learning methods to enhance athletes' tactical decision-making capabilities has become increasingly prevalent. Traditional tactical decision techniques often rely on the experience and knowledge of coaches and video analysis methods that require a lot of time and effort. Consequently, this study proposes a scientific simulation environment for short track speed skating, that accurately simulates the physical attributes of the venue, the physiological fitness of the athletes, and the rules of the competition. The Double Deep Q-Network (DDQN) model is enhanced and utilized, with improvements to the reward function and the distinct description of four tactics. This enables agents to learn optimal tactical decisions in various competitive states with a simulation environment. Experimental results demonstrate that this approach effectively enhances the competition performance and physiological fitness allocation of short track speed skaters.
关键词	short track speed skating deep reinforcement learning decision-making method deep Q-network competition performance improvement
DOI	10.3390/s23249904
关键词[WOS]	FINISHING POSITION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
项目资助者	National Key Research and Development Program of China
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:001130633400001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54908
专题	复杂系统认知与决策实验室
通讯作者	Li, Feimo
作者单位	1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100107, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yang, Yuanbo,Li, Feimo,Chang, Hongxing. Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model[J]. SENSORS,2023,23(24):12.
APA	Yang, Yuanbo,Li, Feimo,&Chang, Hongxing.(2023).Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model.SENSORS,23(24),12.
MLA	Yang, Yuanbo,et al."Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model".SENSORS 23.24(2023):12.