Can multi-agent RL optimize hospital capacity and mobility during epidemics?
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic
March 17, 2025
https://arxiv.org/pdf/2503.10907This paper proposes a multi-agent reinforcement learning (MARL) system, H2-MARL, to optimize restrictions on human movement during epidemics, balancing minimizing hospital strain and the economic/social impact of restricting movement. It uses a simulated environment based on a modified epidemiological model (D-SIHR) with online parameter updates reflecting real-world infection dynamics.
Key points for LLM-based multi-agent systems:
- Dynamic Simulation: The D-SIHR model offers a dynamic, realistic simulation environment crucial for training robust multi-agent systems, especially when combined with an LLM's ability to process and generate complex narratives and scenarios.
- Dual-Objective Optimization: The focus on balancing conflicting objectives (hospital strain vs. movement restriction) highlights the potential for LLMs to integrate ethical considerations and nuanced societal impact evaluations into agent decision-making.
- Expert Knowledge Integration: H2-MARL utilizes expert knowledge to improve agent training, showcasing the opportunity to incorporate LLM-generated insights, rules, and strategies based on vast text corpora and domain expertise.
- Adaptability to Scale: Testing across cities of different sizes suggests the framework could be adapted for diverse, complex scenarios, a strength enhanced by LLMs' ability to generalize and transfer knowledge.
- Agent Collaboration: The multi-agent approach enables coordination across different regions or entities, something LLMs could facilitate by mediating communication and enabling shared understanding between agents.
- Real-world Data Integration: The research emphasizes the importance of real-world datasets (human mobility data), mirroring the valuable context LLMs can derive from extensive real-world text data.
- Online Parameter Updates: The D-SIHR model's online updates reflect the need for continual learning and adaptation, mirroring LLMs' capacity for ongoing training and refinement.