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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 stayat-home orders, while having significant effects, also bring huge economic losses. A crucial question for policymakers around the world is how to make the trade-off and implement the appropriate interventions. In this work, we propose a Multi-Objective Reinforcement Learning framework to facilitate the data-driven decision making and minimize the long-term overall cost. Specifically, at each decision point, a Bayesian epidemiological model is first learned as the environment model, and then we use the proposed model-based multi-objective planning algorithm 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.Preprint. Under review.

Order dispatch is one of the central problems to ridesharing platforms. Recently, value-based reinforcement learning algorithms have shown promising performance on this problem. However, in real-world applications, the non-stationarity of the demandsupply system poses challenges to re-utilizing data generated in different time periods to learn the value function. In this work, motivated by the fact that the relative relationship between the values of some states is largely stable across various environments, we propose a pattern transfer learning framework for value-based reinforcement learning in the order dispatch problem. Our method efficiently captures the value patterns by incorporating a concordance penalty. The superior performance of the proposed method is supported by experiments.