Alleviating Community Fear in Disasters via Multi-Agent Actor-Critic Reinforcement Learning
For disaster management agencies, this work provides a novel method to actively reduce community fear during cascading failures, with demonstrated generalizability across hurricanes.
This paper extends a cyber-physical-social resilience model with control channels for three agencies and formulates it as a differential game solved via multi-agent actor-critic reinforcement learning. Simulations based on Hurricane Harvey data show 70% mean fear reduction, with cross-validation on Hurricane Irma achieving 50% fear reduction.
During disasters, cascading failures across power grids, communication networks, and social behavior amplify community fear and undermine cooperation. Existing cyber-physical-social (CPS) models simulate these coupled dynamics but lack mechanisms for active intervention. We extend the CPS resilience model of Valinejad and Mili (2023) with control channels for three agencies, communication, power, and emergency management, and formulate the resulting system as a three-player non-zero-sum differential game solved via online actor-critic reinforcement learning. Simulations based on Hurricane Harvey data show 70% mean fear reduction with improved infrastructure recovery; cross-validation in the case of Hurricane Irma (without refitting) achieves 50% fear reduction, confirming generalizability.