Multi-agent Reinforcement Learning Optimization for Urban Earthquake Emergency Evacuation with GIS-based Real-time Decision Support

Urban earthquake evacuations require the use of real-time, automated computer decision-support systems, which are based on data and can handle risks that are changing and impose restrictions on mobility. In this paper, a framework for large-scale evacuation optimization is offered, which consists of a risk-aware Multi-Agent Reinforcement Learning (MARL) integrated with a GIS-based Digital Twin. The framework presented here uses a Conditional Value-at-Risk (CVaR) objective to augment the robustness of the policy in the face of significant and rare disruptions; at the same time, a multi-layered architecture (data fusion, digital twin, CTDE learning, real-time guidance) assures coherence in operations. The scalability and reliability of the system were tested through the application of 1,200 earthquake scenarios that were created using the fusion of GIS, IoT, and mobility data that are statistically representative. The results of the experiments show that the CVaR-MARL model proposed has about 10–12% lower variance in clearance time and much more improved stability compared to the traditional MARL and PPO methods. The contributions of this paper are as follows: (1) development of a risk-sensitive learning paradigm for real-time evacuation, (2) a GIS-IoT digital twin that can be used for scenario simulation, and (3) comprehensive evaluation on latency, robustness, and interpretability. The implications of these findings are that risk-aware MARL has a very strong potential for being used in urban crisis management and decision support systems in real-time situations.