Epidemics such as COVID-19 pose serious threats to public health and our society, and it is critical to investigate effective methods to control the spread of epidemics over networks. Prior works on epidemic control often assume complete knowledge of network structures, a presumption seldom valid in real-world situations. In this paper, we study epidemic control on networks with unknown structures, and propose a hierarchical reinforcement learning framework for joint network structure exploration and epidemic control. To reduce the action space and achieve computation tractability, our proposed framework contains three modules: the Policy Selection Module, which determines whether to explore the structure or remove nodes to control the epidemic; the Explore Module, responsible for selecting nodes to explore; and the Remove Module, which decides which nodes to remove to stop the epidemic spread. Simulation results show that our proposed method outperforms baseline methods.

翻译：诸如COVID-19等流行病对公众健康和社会构成严重威胁，研究有效控制网络传播的方法至关重要。以往关于流行病控制的研究通常假设网络结构完全已知，这一假设在实际场景中鲜少成立。本文针对未知结构网络上的流行病控制问题展开研究，提出了一种联合网络结构探索与流行病控制的层次强化学习框架。为缩减动作空间并实现计算可行性，该框架包含三个模块：策略选择模块（决定探索结构还是移除节点以控制疫情）、探索模块（负责选择待探索节点）以及移除模块（决策需移除哪些节点以阻止疫情传播）。仿真结果表明，所提方法优于基线方法。