Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery

After a city-scale natural hazard, policymakers should plan sound decisions on the repair sequence to ensure the resilient recovery of the community, which consists of interdependent infrastructures. Stochastic scheduling for repairing interdependent infrastructure systems is a difficult control problem with huge decision spaces. This study proposes a novel decision support model to determine the optimal restoration policies for the purpose of maximizing disaster resilience. A simulation environment is first developed, consisting of hazard intensity assessment, components damage evaluation, system recovery simulation, and resilience quantification. The graph theory is utilized to represent the interdependencies among different systems, and the heterogeneous graph neural network is integrated into this framework to extract the topology and interdependency information of the whole community. The optimal repair policies approximated by neural networks are trained by a multi-agent deep reinforcement learning algorithm, considering uncertainties of the restoration process. The superiority and efficiency of the proposed method are demonstrated through a case study of the Tsinghua University campus, where different decision-making objectives are considered. The results show that the recovery trajectories determined by the proposed model have the highest performance compared to conventional methods. Besides, the proposed methodology based on transfer learning can achieve high computational efficiency for new damage scenarios. This model is promising to be a high-performance, robust decision-support tool for post-hazard repairing decisions.