Reinforcement Learning Algorithm Helps Optimize Personalized Cognitive Behavioral Therapy for Anxiety Disorders

This paper explores the application of reinforcement learning (RL) algorithms in optimizing personalized cognitive behavioral therapy (CBT) for anxiety disorders. Traditional CBT often lacks personalization, leading to variable efficacy. RL addresses this by dynamically adjusting treatment strategies through reward/punishment feedback, aiming to maximize long-term outcomes. Experimental results show that RL-based CBT improves treatment efficacy by 27– 63% compared to conventional methods, significantly reducing anxiety symptoms and enhancing quality of life. This study aims to advance precision mental healthcare by tailoring interventions to individual needs, contributing a novel framework for personalized treatment.