# A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.06589 · HTML: https://mlsanity.com/paper/2108.06589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence](https://mlsanity.com/paper/1712.00600/) (2017)
- [Reinforcement Learning for Optimization of COVID-19 Mitigation policies](https://mlsanity.com/paper/2010.10560/) (2020)
- [COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions](https://mlsanity.com/paper/2006.10532/) (2020)
- [Modelling transmission and control of the COVID-19 pandemic in Australia](https://mlsanity.com/paper/2003.10218/) (2020)
- [Exploiting Fast Decaying and Locality in Multi-Agent MDP with Tree Dependence Structure](https://mlsanity.com/paper/1909.06900/) (2019)
- [A Microscopic Epidemic Model and Pandemic Prediction Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2004.12959/) (2020)

## Built on by (most cited first, 1 of 1)

- [Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models](https://mlsanity.com/paper/2606.04562/) (2026)

## Abstract (arXiv)

Microscopic epidemic models are powerful tools for government policy makers to predict and simulate epidemic outbreaks, which can capture the impact of individual behaviors on the macroscopic phenomenon. However, existing models only consider simple rule-based individual behaviors, limiting their applicability. This paper proposes a deep-reinforcement-learning-powered microscopic model named Microscopic Pandemic Simulator (MPS). By replacing rule-based agents with rational agents whose behaviors are driven to maximize rewards, the MPS provides a better approximation of real world dynamics. To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN). The MPS allows us to efficiently evaluate the impact of different government strategies. This paper first calibrates the MPS against real-world data in Allegheny, US, then demonstratively evaluates two government strategies: information disclosure and quarantine. The results validate the effectiveness of the proposed method. As a broad impact, this paper provides novel insights for the application of DRL in large scale agent-based networks such as economic and social networks.