Multi-Agent Deep Reinforcement Learning based Interdependent Critical Infrastructure Simulation Model for Situational Awareness during a Flood Event

Multi-Agent Deep Reinforcement Learning based Interdependent Critical Infrastructure Simulation Model for Situational Awareness during a Flood Event

연구 분야: Infrastructure

학회: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

The paper proposes a Multi-Agent Deep Reinforcement Learning (MADRL) simulation model that is useful in understanding the status of Critical Infrastructures (CI) during extreme events. The simulation model can be used to understand the spatiotemporal nature of the event and evaluate and predict the propagation of cascading failure scenarios in the critical infrastructure network. Multi agent-based modeling is performed by interconnecting multiple agents, which are autonomous computational entities. Geospatial based intelligent agents are developed, such that each agent registers with a CI such as a Healthcare infrastructure agent, Transportation agent, etc. These agents check for an infrastructure state change (e.g. the roads which lead to the hospital are blocked due to debris), and if there is a state change then they would reason about the impacts of these events upon other dependent infrastructures. Deep reinforcement learning approach helps the geospatial based CI agents in making a rapid and an optimal decision based on its spatiotemporal environment, during a flood event. The utility of the approach is evaluated using a real-world case study. Real-time information simulation would help disaster response personnel to respond to the question, `what if something else happens?