Spread Control Method on Unknown Networks Based on Hierarchical Reinforcement Learning

TL;DR

This paper introduces a hierarchical reinforcement learning approach for controlling epidemics on networks with unknown structures, jointly exploring network topology and implementing control actions to effectively mitigate spread.

Contribution

It proposes a novel hierarchical reinforcement learning framework that combines network exploration and epidemic control without prior network knowledge.

Findings

Outperforms baseline methods in simulations

Effectively explores unknown network structures

Achieves better epidemic control results

Abstract

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.