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Reinforcement Learning for Optimization of COVID-19 Mitigation policies

arXiv 2010.10560 ↗First posted 20 Oct 2020cs.LG cs.AI cs.CYcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 4; 2024: 3; 2025: 4; 2026: 5.

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  1. 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 312
  2. 2024Correlated Proxies: A New Definition and Improved Mitigation for Reward Hackingcited by 50
  3. 2021Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Datacited by 18
  4. 2022Testing Stationarity and Change Point Detection in Reinforcement Learningcited by 12
  5. 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
  6. 2021Estimating the State of Epidemics Spreading with Graph Neural Networkscited by 9
  7. 2022Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learningcited by 9
  8. 2022Interpretable AI for policy-making in pandemicscited by 5
  9. 2023Planning Multiple Epidemic Interventions with Reinforcement Learningcited by 4
  10. 2022A Direct Approximation of AIXI Using Logical State Abstractionscited by 2
  11. 2021A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learningcited by 1
  12. 2024Privacy Preserving Reinforcement Learning for Population Processescited by 1
13 more
  1. 2025Repairing Reward Functions with Feedback to Mitigate Reward Hackingcited by 1
  2. 2023A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysiscited by 0
  3. 2023Epidemic Decision-making System Based Federated Reinforcement Learningcited by 0
  4. 2023Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemiccited by 0
  5. 2024Social Interpretable Reinforcement Learningcited by 0
  6. 2025Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic datacited by 0
  7. 2025Learning Pareto-Optimal Pandemic Intervention Policies with MORLcited by 0
  8. 2025Do You Trust the Process?: Modeling Institutional Trust for Community Adoption of Reinforcement Learning Policiescited by 0
  9. 2026Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learningcited by 0
  10. 2026When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?cited by 0
  11. 2026Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Modelscited by 0
  12. 2026Specifying Reward Functions for RL Without Environment Samplingcited by 0
  13. 2026Graph World Models for Constrained Epidemic Policy Planningcited by 0