AI-Driven Policy Testing for Mental Health Crisis Response: An Agent-Based Modelling and Reinforcement Learning Approach

This study introduces a novel simulation-based framework that integrates Agent-Based Modelling (ABM) with Reinforcement Learning (RL) to evaluate and optimize policies for mental health crisis response. As mental health crises become increasingly complex and context-specific, traditional fixed-resource strategies may fail to adapt to evolving population needs. To address this, we simulate a diverse synthetic population characterized by varying demographic attributes such as age and gender, as well as factors like baseline mental health, stress exposure, social support, and access to care. Agents evolve over time based on stochastic stressors and receive interventions through three modelled resource types: hotlines, counselling services, and emergency care. We compare four policy strategies: hotline-only, counselling-only, a mixed-resource approach, and a PPO-trained RL policy designed to dynamically allocate resources based on real-time population states. Each strategy is simulated over a 100-day period. Key evaluation metrics include crisis rate, intervention coverage, unmet need rate, average stress level, total interventions, and a Policy Efficiency Score (PES). Spatial resource usage and demographic subgroup outcomes are also tracked and analysed. Our results reveal that counselling-focused strategies offer the most sustainable balance of low crisis rates and stress levels with moderate intervention coverage. While the RL-optimized policy achieves 100% intervention coverage and zero unmet needs, it also maintains the highest average stress, suggesting an over-saturation of interventions without long-term mental health relief. The findings underscore the importance of not only maximizing access but also prioritizing effective and sustainable care. This framework serves as a decision-support tool to guide public health resource allocation and policy design in crisis settings.