Multi-agent-based emergency supplies dispatch

Abstract

In urban assistance scenarios, the problem of resource allocation on the urban streets is essentially a specialized form of intelligent traffic control, where the logistics and security capabilities greatly influence efficiency of urban rescue operations. Currently, due to the lack of simulated training environments for urban assistance, solving such problems relies solely on theoretical deductions, providing limited quantitative basis for individual resource allocation and support. To simulate realistic city rescue logistics, this paper utilizes the NetLogo simulation environment to construct a two-dimensional traffic flow grid model, simplifying the urban traffic scenario. The emerging DQN reinforcement learning algorithm is employed in the software to enhance the transportation scheduling capabilities of multiple agents, addressing resource allocation challenges in various scenarios on the city rescue. This approach effectively tackles complex real-world problems and holds promising applications