Resilience-based post disaster recovery optimization for infrastructure system via deep reinforcement learning

Infrastructure systems are essential yet vulnerable to natural and man-made disasters. Efficient post-disaster recovery requires repair-scheduling approaches under limited resources shared across the system. Existing approaches like component ranking, greedy algorithms, and data-driven models often lack resilience orientation, adaptability, and require high computational resources when tested within such a context. To tackle these issues, we propose a solution by leveraging Deep Reinforcement Learning (DRL) methods and a specialized resilience metric to lead the recovery optimization. The system topology is represented adopting a graph-based structure, where the system’s recovery process is formulated as a sequential decision-making problem. Deep Q-learning algorithms are employed to learn optimal recovery strategies by mapping system states to specific actions, i.e., which component ought to be repaired next, with the goal of maximizing long-term recovery from a resilience-oriented perspective. To demonstrate the efficacy of our proposed approach, we implement this scheme on the example of post-earthquake recovery optimization for an electrical substation system. A comparative analysis against baseline methods reveals the superior performance of the proposed method in terms of both optimization effect and computational cost, rendering this an attractive approach in the context of resilience enhancement and rapid response and recovery.