Reinforcement learning based framework for COVID-19 resource allocation

In this paper, a reinforcement learning based framework is developed for COVID-19 resource allocation. We first construct an agent-based epidemic environment to model the transmission dynamics in multiple states. Then, a multi-agent reinforcement-learning algorithm is proposed based on the time-varying properties of the environment, and the performance of the algorithm is compared with other algorithms. According to the age distribution of populations and their economic conditions, the optimal lockdown resource allocation strategies of Arizona, California, Nevada, and Utah in the United States are determined using the proposed reinforcement-learning algorithm. Experimental results show that the framework can adopt more flexible resource allocation strategies and help decision makers to determine the optimal deployment of limited resources in infection prevention.