Advancing AIoMT-Enabled Healthcare System-of-Systems Using Multi-Agent Reinforcement Learning

Abstract

This paper presents a Multi-Agent Reinforcement Learning (MARL) framework designed to optimize coordination within a System-of-Systems (SoS) composed of six heterogeneous healthcare entities: hospitals, clinics, telemedicine platforms, wearable monitoring devices, rural health centers, and virtual triage hubs. These entities are organized into four distinct constituent systems—Directed, Acknowledged, Collaborative, and Virtual—each reflecting a unique coordination model that governs control, autonomy, and communication. Within each constituent system, agents independently learn optimal policies using Q-learning to enhance resource allocation, inter-agent cooperation, and service efficiency. We describe simulation experiments that span 1000 episodes, with evaluation metrics including cumulative rewards, resource utilization, and system-level efficiency. The results show that Acknowledged and Collaborative coordination models achieve faster policy convergence and superior operational outcomes, while the Virtual model provides flexibility with reduced coordination overhead. Our framework demonstrates the potential of MARL to enable adaptive, decentralized decision-making in Artificial Intelligence of Medical Things (AIoMT)-enabled healthcare environments, particularly under dynamic and resource-constrained conditions. Importantly, this approach can help improve care access in remote and underserved regions and enable more dynamic, responsive triage and resource allocation in real-world healthcare delivery.