Deep Reinforcement Learning Driven Critical Infrastructure Protection During Extreme Events

Critical infrastructure (CI) plays a pivotal role in supporting daily life, encompassing vital sectors such as Healthcare, Transportation, and Power systems. Failure of CI can significantly impede everyday activities, making it essential to understand and address the interdependencies among these infrastructures. Failures can arise from both natural and manmade events, often leading to cascading effects across CI sectors. To mitigate capital loss and adversity during such failures, the efficient utilization of limited resources becomes crucial. This paper introduces a real-time decision-making system that incorporates local and global factors impacting CI and enables effective resource allocation. In this study, we focus on the failure of a specific CI sector, healthcare, caused by a flood event. To simulate this scenario, we create an environment that captures interdependencies between CI sectors, while generating flood events through randomly distributed water levels over time. To optimize decision-making, we employ a Reinforcement Learning (RL) based agent trained using Deep Q learning. The trained agent suggests critical decisions that enhance the utilization of limited resources, thereby extending the system's survivability. To provide a comprehensive view of the system's state and actions recommended by the learned agent at each time step (t), we develop a user interface. This interface displays the environment's states and facilitates the visualization of alternative CI protection strategies during catastrophic events like floods. Such simulation environments empower decision-makers with vital capabilities to make informed choices regarding resource allocation in critical scenarios.