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

Yuanbo Yang¹, Feimo Li², Hongxing Chang²

¹School of Information Science and Technology, Beijing Forestry University Beijing, Beijing 100107, China.

Sensors (Basel, Switzerland)

|December 23, 2023

Summary

This study uses deep reinforcement learning to create a tactical decision-making model for short track speed skating. The AI approach enhances athlete performance and optimizes physiological fitness allocation in competition.

Keywords:

competition performance improvement decision-making method deep Q-network deep reinforcement learning short track speed skating

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Area of Science:

Sports Science
Artificial Intelligence
Computational Science

Background:

Short track speed skating is a popular Winter Olympic sport.
Traditional tactical training relies on coach experience and time-consuming video analysis.
Advancements in AI offer new methods for enhancing athlete decision-making.

Purpose of the Study:

To develop an AI-driven tactical decision-making model for short track speed skating.
To improve competitive performance and physiological fitness allocation for skaters.
To create a scientific simulation environment for training and analysis.

Main Methods:

Developed a scientific simulation environment for short track speed skating.
Enhanced the Double Deep Q-Network (DDQN) model for tactical decision-making.
Incorporated improved reward functions and defined four distinct tactics for AI agents.

Main Results:

AI agents learned optimal tactical decisions within the simulation environment.
Demonstrated effective enhancement of competition performance.
Showcased improved physiological fitness allocation for skaters.

Conclusions:

Deep reinforcement learning provides an effective framework for tactical optimization in short track speed skating.
The proposed AI model and simulation environment can significantly benefit athlete training and performance.
This approach offers a data-driven, efficient alternative to traditional training methods.