Multi-disaster Emergency Response Decision Support Based On Reinforcement Learning Algorithm

With the acceleration of global climate change and urbanization, the frequency and intensity of natural disasters and emergencies continue to increase, posing severe challenges to social security and stability. Traditional emergency response research often focuses on a single type of disaster, relying on static models and historical data, and lacking in-depth analysis of the interaction between multiple disasters. This article investigated a multi disaster emergency response decision support system based on reinforcement learning. The system combined real-time data with reinforcement learning algorithms by constructing a dynamic environment model, achieving adaptive decision-making of intelligent agents in disaster scenarios. In terms of specific methods, this article designed state, action, and reward mechanisms to optimize emergency response strategies, and evaluated the performance of the system through simulation experiments. The experimental results showed that the decision support system based on reinforcement learning significantly improved decision efficiency (Max: 89.4%) and resource utilization (Max: 91.4%) in multi disaster emergency response. This study not only provides a new solution for multi disaster emergency response, but also lays the foundation for the intelligent development of future emergency management.