Reinforcement Learning-Based Q-Learning Approach for Optimizing Data Mining in Dynamic Environments

This study proposed a new data mining strategy based on the reinforcement learning Q-learning method to solve complex decision-making optimization problems in dynamic environments. By modeling the data mining task as a Markov decision process, the study designed a reasonable state representation, reward mechanism and strategy update method, and experimentally verified it on a real data set. The results show that this method outperforms traditional machine learning models and deep learning models in key indicators such as cumulative return value and accuracy, showing strong generalization and global optimization capabilities. The study also shows that the Q-learning method can adapt to time series and highly dynamic environments, providing an effective solution for optimizing long-term returns. In the future, combining deep learning technology with more complex practical application scenarios, this method is expected to further enhance the intelligence and real-time performance of data mining, laying a solid foundation for its widespread application in the field of artificial intelligence.