# Reinforcement Learning for Optimization of COVID-19 Mitigation policies

arXiv: https://arxiv.org/abs/2010.10560 · HTML: https://mlsanity.com/paper/2010.10560/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models](https://mlsanity.com/paper/2201.03544/) (2022)
- [Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking](https://mlsanity.com/paper/2403.03185/) (2024)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Testing Stationarity and Change Point Detection in Reinforcement Learning](https://mlsanity.com/paper/2203.01707/) (2022)
- [Estimating the State of Epidemics Spreading with Graph Neural Networks](https://mlsanity.com/paper/2105.05060/) (2021)
- [Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning](https://mlsanity.com/paper/2204.05027/) (2022)
- [Interpretable AI for policy-making in pandemics](https://mlsanity.com/paper/2204.04256/) (2022)
- [Planning Multiple Epidemic Interventions with Reinforcement Learning](https://mlsanity.com/paper/2301.12802/) (2023)
- [A Direct Approximation of AIXI Using Logical State Abstractions](https://mlsanity.com/paper/2210.06917/) (2022)
- [A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.06589/) (2021)
- [Privacy Preserving Reinforcement Learning for Population Processes](https://mlsanity.com/paper/2406.17649/) (2024)
- [Repairing Reward Functions with Feedback to Mitigate Reward Hacking](https://mlsanity.com/paper/2510.13036/) (2025)
- [A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis](https://mlsanity.com/paper/2311.00855/) (2023)
- [Epidemic Decision-making System Based Federated Reinforcement Learning](https://mlsanity.com/paper/2311.01749/) (2023)
- [Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic](https://mlsanity.com/paper/2312.15163/) (2023)
- [Social Interpretable Reinforcement Learning](https://mlsanity.com/paper/2401.15480/) (2024)
- [Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data](https://mlsanity.com/paper/2505.04161/) (2025)
- [Learning Pareto-Optimal Pandemic Intervention Policies with MORL](https://mlsanity.com/paper/2510.03340/) (2025)
- [Do You Trust the Process?: Modeling Institutional Trust for Community Adoption of Reinforcement Learning Policies](https://mlsanity.com/paper/2510.22017/) (2025)
- [Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2603.19397/) (2026)
- [When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?](https://mlsanity.com/paper/2605.30719/) (2026)
- [Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models](https://mlsanity.com/paper/2606.04562/) (2026)
- [Specifying Reward Functions for RL Without Environment Sampling](https://mlsanity.com/paper/2609.15544/) (2026)
- [Graph World Models for Constrained Epidemic Policy Planning](https://mlsanity.com/paper/2609.35545/) (2026)

## Abstract (arXiv)

The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date,the even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic impact without overwhelming the hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; and (2) an RL-based methodology for optimizing fine-grained mitigation policies within this simulator. Our results validate both the overall simulator behavior and the learned policies under realistic conditions.