Reinforcement Learning-Based Framework for Dynamic Strategy Generation in Personalized Psychological Counseling Using Deep Q-Networks
Abstract
Personalized psychological counseling plays a crucial role in enhancing mental well-being by addressing individual emotional and cognitive needs. This study proposes a Reinforcement Learning-Based Decision Support Framework (RL-DSF) that dynamically generates counseling strategies optimized through a Deep Q-Network (DQN). The model adapts in real-time to users' evolving psychological states by leveraging feedback signals derived from emotional responses, engagement metrics, and counseling effectiveness. The RL-DSF was trained and evaluated using a synthetic therapy conversations dataset, comprising diverse simulated dialogues with annotated emotional cues, designed to mimic real-world mental health scenarios. While no direct clinical patient data was used in training, the system’s effectiveness was assessed on anonymized user sessions collected from a chatbot-based mental health support platform. Experimental results demonstrated that RL-DSF significantly outperformed baseline methods, achieving an average reduction of 1.1 points on PHQ-9 depression scores and 0.6 points on GAD-7 anxiety scores. User engagement increased by 11.1%, satisfaction ratings averaged 4.5 out of 5, and dropout rates were reduced to 5%, validating the framework’s potential to provide adaptive, personalized psychological support in a scalable digital environment.References
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