# Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

arXiv: https://arxiv.org/abs/2204.05027 · HTML: https://mlsanity.com/paper/2204.05027/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions](https://mlsanity.com/paper/1912.02875/) (2019)
- [Dynamic Weights in Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1809.07803/) (2018)
- [Reinforcement Learning for Optimization of COVID-19 Mitigation policies](https://mlsanity.com/paper/2010.10560/) (2020)
- [Deep reinforcement learning for large-scale epidemic control](https://mlsanity.com/paper/2003.13676/) (2020)
- [Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies](https://mlsanity.com/paper/1711.06299/) (2017)
- [VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning](https://mlsanity.com/paper/2009.06602/) (2020)
- [Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control](https://mlsanity.com/paper/2009.04607/) (2020)

## Built on by (most cited first, 9 of 9)

- [Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2211.13032/) (2022)
- [Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance](https://mlsanity.com/paper/2411.18195/) (2024)
- [Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models](https://mlsanity.com/paper/2207.00368/) (2022)
- [Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration](https://mlsanity.com/paper/2301.12822/) (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)
- [Needles in the Landscape: Semi-Supervised Pseudolabeling for Archaeological Site Discovery under Label Scarcity](https://mlsanity.com/paper/2510.16814/) (2025)
- [On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19](https://mlsanity.com/paper/2511.18035/) (2025)
- [TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2603.21988/) (2026)
- [Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits](https://mlsanity.com/paper/2606.18785/) (2026)

## Abstract (arXiv)

Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models. Current research focuses on optimizing policies w.r.t. a single objective, such as the pathogen's attack rate. However, as the mitigation of epidemics involves distinct, and possibly conflicting criteria (i.a., prevalence, mortality, morbidity, cost), a multi-objective approach is warranted to learn balanced policies. To lift this decision-making process to real-world epidemic models, we apply deep multi-objective reinforcement learning and build upon a state-of-the-art algorithm, Pareto Conditioned Networks (PCN), to learn a set of solutions that approximates the Pareto front of the decision problem. We consider the first wave of the Belgian COVID-19 epidemic, which was mitigated by a lockdown, and study different deconfinement strategies, aiming to minimize both COVID-19 cases (i.e., infections and hospitalizations) and the societal burden that is induced by the applied mitigation measures. We contribute a multi-objective Markov decision process that encapsulates the stochastic compartment model that was used to inform policy makers during the COVID-19 epidemic. As these social mitigation measures are implemented in a continuous action space that modulates the contact matrix of the age-structured epidemic model, we extend PCN to this setting. We evaluate the solution returned by PCN, and observe that it correctly learns to reduce the social burden whenever the hospitalization rates are sufficiently low. In this work, we thus show that multi-objective reinforcement learning is attainable in complex epidemiological models and provides essential insights to balance complex mitigation policies.