Treffer: Enhanced reinforcement learning using fuzzy cognitive modeling to analyze mental toughness in college athletes.

In the field of competitive sports, psychological resilience is one of the key factors for athletes to succeed in a high-pressure, high-intensity competitive environment. It is particularly important for college athletes who face the dual pressure of academic and sports. However, current research on athletes' psychological resilience is mostly concentrated in the fields of psychology and social sciences. There are few studies on the use of advanced technology systems to improve psychological resilience in practical applications in the field of sports and lack of research on the combination of fuzzy cognitive mapping (FCM) and reinforcement learning (RL). This study explored innovative methods by combining FCM and RL to build a fuzzy cognitive model based on expert knowledge and empirical data, using Python and related libraries to design a reinforcement learning framework based on the Q-learning algorithm, and collecting data through questionnaires and interviews. The results showed that the weight of adversity adaptability in the fuzzy cognitive model was the highest one of 0.3. The average score of psychological resilience in the experimental group after reinforcement learning training increased from 60 to 75, and the key factors were significantly improved, while the control group did not improve significantly. The comprehensive score of psychological resilience of athlete participant increased significantly after training. This study successfully constructed a theoretical and practical method integrating FCM and RL, providing a new perspective for understanding the development of athletes' psychological resilience. [ABSTRACT FROM AUTHOR]

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