论文标题
基于多目标的基于模型的强化学习，用于传染病控制
Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
论文作者
论文摘要
新型冠状病毒（Covid-19）等严重的传染病对公共卫生构成了巨大威胁。严格的控制措施，例如关闭学校和全职命令，同时产生重大影响，也带来了巨大的经济损失。面对一种新兴的传染病，对决策者来说，一个关键的问题是如何使权衡并及时实施适当的干预措施，鉴于巨大的不确定性。在这项工作中，我们提出了一个基于多目标的基于模型的增强学习框架，以促进数据驱动的决策并最大程度地降低整体长期成本。具体而言，在每个决策点上，首先将贝叶斯流行病学模型作为环境模型学习，然后将基于模型的多目标计划算法用于查找一组帕托托最佳策略。该框架与每个策略的预测频段相结合，为决策者提供了实时决策支持工具。该应用程序是通过19日在中国传播的。
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health. Stringent control measures, such as school closures and stay-at-home orders, while having significant effects, also bring huge economic losses. In the face of an emerging infectious disease, a crucial question for policymakers is how to make the trade-off and implement the appropriate interventions timely given the huge uncertainty. In this work, we propose a Multi-Objective Model-based Reinforcement Learning framework to facilitate data-driven decision-making and minimize the overall long-term cost. Specifically, at each decision point, a Bayesian epidemiological model is first learned as the environment model, and then the proposed model-based multi-objective planning algorithm is applied to find a set of Pareto-optimal policies. This framework, combined with the prediction bands for each policy, provides a real-time decision support tool for policymakers. The application is demonstrated with the spread of COVID-19 in China.
