Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model
文献类型：期刊论文
| 作者 | Yang, Yuanbo1; Li, Feimo2 ; Chang, Hongxing2
|
| 刊名 | SENSORS
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 23期号:24页码:12 |
| 关键词 | short track speed skating deep reinforcement learning decision-making method deep Q-network competition performance improvement |
| DOI | 10.3390/s23249904 |
| 通讯作者 | Li, Feimo(lifeimo2012@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | FINISHING POSITION |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001130633400001 |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54907]  |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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