用强化学习协调三类应急机构，降低灾害中的社区恐惧。
Alleviating Community Fear in Disasters via Multi-Agent Actor-Critic Reinforcement Learning
- 构建三机构协同控制的非零和微分博弈模型
- 模拟显示平均恐惧降低70%，飓风哈维数据验证有效
- 跨案例验证通用性，适合应急管理与智能决策研究者
灾害期间，电力网络、通信系统与社会行为的级联失效会加剧社区恐惧并破坏合作。现有信息-物理-社会（CPS）模型虽能模拟这些耦合动态，但缺乏主动干预机制。本文在Valinejad和Mili（2023）的CPS韧性模型基础上，引入通信、电力与应急管理部门的控制通道，将系统建模为三玩家非零和微分博弈，并采用在线演员-评论家强化学习求解。基于飓风哈维数据的仿真显示，平均恐惧降低70%，基础设施恢复显著改善；在未重新训练的情况下对飓风艾尔玛进行交叉验证，恐惧降低50%，证实了模型的泛化能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。