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H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic by Luo, Xueting; Deng, Hao; Yang, Jihong; Shen, Yao; Guo, Huanhuan; Sun, Zhiyuan; Liu, Mingqing; Wei, Jiming; Zhao, Shengjie is a scholarly article available to read on EtoBox.

What is H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic about?

The necessity of achieving an effective balance between minimizing the losses associated with restricting human mobility and ensuring hospital capacity has gained significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities and epidemics; however, they still face challenges in achieving coordinated control at the township level and adapting to cities of varying scales. To address the above issues, we propose a multi-agent RL approach that achieves Pareto optimality in managing hospital capacity and human mobility (H2-MARL), applicable across cities of different scales. We first develop a township-level infection model with online-updatable parameters to simulate disease transmission and construct a city-wide dynamic spatiotemporal epidemic simulator. On this basis, H2-MARL is designed to treat each division as an agent, with a trade-off dual-objective reward function formulated and an experience replay buffer enriched with expert knowledge built. To evaluate the effectiveness of the model, we construct a township-level human mobility dataset contain

Author

Luo, Xueting; Deng, Hao; Yang, Jihong; Shen, Yao; Guo, Huanhuan; Sun, Zhiyuan; Liu, Mingqing; Wei, Jiming; Zhao, Shengjie

Published

2025

Language

EN