Reinforcement Learning-Driven Proximal Policy Optimization on Mental Health Monitoring

Abstract

Millions of people worldwide suffer from mental health illnesses, which make diagnosis, treatment, and individualized care extremely difficult. Conventional therapy methods frequently find it difficult to dynamically adjust to the needs of each patient. By improving decisionmaking in intricate and dynamic contexts, reinforcement learning (RL), and in particular proximal policy optimization (PPO), presents a viable AI-driven solution. PPO is a good option for mental health applications like individualized therapy suggestions and intervention scheduling because of its stability, sample effectiveness, and capacity to manage ongoing action spaces. The use of PPO in mental-health care is examined in this research, with an emphasis on how it might improve AI-driven solutions while addressing important ethical issues including patient privacy, fairness, and transparency. We examine PPO's function in maximizing treatment approaches, go over current developments in RL included mental health models, and talk about the difficulties of incorporating AI into clinical practice. PPO can greatly enhance individualized treatment plans, according to our findings, but ethical limitations and practical implementation issues need to be carefully taken into account. Effective, ethical mental health interventions and AI-driven reinforcement learning are becoming closer appreciations to this research.

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