# Learning Pareto-Optimal Pandemic Intervention Policies with MORL

arXiv: https://arxiv.org/abs/2510.03340 · HTML: https://mlsanity.com/paper/2510.03340/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 8 of 8)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Reinforcement Learning for Optimization of COVID-19 Mitigation policies](https://mlsanity.com/paper/2010.10560/) (2020)
- [Pareto Conditioned Networks](https://mlsanity.com/paper/2204.05036/) (2022)
- [Deep reinforcement learning for large-scale epidemic control](https://mlsanity.com/paper/2003.13676/) (2020)
- [Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning](https://mlsanity.com/paper/2003.14093/) (2020)
- [Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control](https://mlsanity.com/paper/2009.04607/) (2020)
- [Planning Multiple Epidemic Interventions with Reinforcement Learning](https://mlsanity.com/paper/2301.12802/) (2023)

## Abstract (arXiv)

The COVID-19 pandemic underscored a critical need for intervention strategies that balance disease containment with socioeconomic stability. We approach this challenge by designing a framework for modeling and evaluating disease-spread prevention strategies. Our framework leverages multi-objective reinforcement learning (MORL) - a formulation necessitated by competing objectives - combined with a new stochastic differential equation (SDE) pandemic simulator, calibrated and validated against global COVID-19 data. Our simulator reproduces national-scale pandemic dynamics with orders of magnitude higher fidelity than other models commonly used in reinforcement learning (RL) approaches to pandemic intervention. Training a Pareto-Conditioned Network (PCN) agent on this simulator, we illustrate the direct policy trade-offs between epidemiological control and economic stability for COVID-19. Furthermore, we demonstrate the framework's generality by extending it to pathogens with different epidemiological profiles, such as polio and influenza, and show how these profiles lead the agent to discover fundamentally different intervention policies. To ground our work in contemporary policymaking challenges, we apply the model to measles outbreaks, quantifying how a modest 5% drop in vaccination coverage necessitates significantly more stringent and costly interventions to curb disease spread. This work provides a robust and adaptable framework to support transparent, evidence-based policymaking for mitigating public health crises.