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：流行病如COVID-19对公众健康和社会构成严重威胁，研究有效控制疫情在网络中传播的方法至关重要。以往的疫情控制研究通常假设网络结构完全已知，这一假设在真实场景中很少成立。本文研究结构未知网络上的疫情控制问题，并提出一种分层强化学习框架，用于联合网络结构探索和疫情控制。为缩减动作空间并实现计算可行性，所提框架包含三个模块：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）和移除模块（决策移除哪些节点以阻断疫情传播）。仿真结果表明，所提方法优于基线方法。