# Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control

arXiv: https://arxiv.org/abs/2009.04607 · HTML: https://mlsanity.com/paper/2009.04607/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning](https://mlsanity.com/paper/2204.05027/) (2022)
- [Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring](https://mlsanity.com/paper/2304.00420/) (2023)
- [Decision-Focused Model-based Reinforcement Learning for Reward Transfer](https://mlsanity.com/paper/2304.03365/) (2023)
- [H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic](https://mlsanity.com/paper/2503.10907/) (2025)
- [Learning Pareto-Optimal Pandemic Intervention Policies with MORL](https://mlsanity.com/paper/2510.03340/) (2025)
- [Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learning](https://mlsanity.com/paper/2511.18000/) (2025)
- [On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19](https://mlsanity.com/paper/2511.18035/) (2025)

## Abstract (arXiv)

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.