# Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic

arXiv: https://arxiv.org/abs/2312.15163 · HTML: https://mlsanity.com/paper/2312.15163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 23 Dec 2023.

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

- [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)
- [Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning](https://mlsanity.com/paper/2003.14093/) (2020)
- [COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning](https://mlsanity.com/paper/2009.04647/) (2020)
- [Novel Exploration Techniques (NETs) for Malaria Policy Interventions](https://mlsanity.com/paper/1712.00428/) (2017)
- [Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study](https://mlsanity.com/paper/2203.13395/) (2022)
- [Data-driven control of COVID-19 in buildings: a reinforcement-learning approach](https://mlsanity.com/paper/2212.13559/) (2022)

## Abstract (arXiv)

Epidemic modeling, encompassing deterministic and stochastic approaches, is vital for understanding infectious diseases and informing public health strategies. This research adopts a prescriptive approach, focusing on reinforcement learning (RL) to develop strategies that balance minimizing infections with maximizing in-person interactions in educational settings. We introduce SafeCampus , a novel tool that simulates infection spread and facilitates the exploration of various RL algorithms in response to epidemic challenges. SafeCampus incorporates a custom RL environment, informed by stochastic epidemic models, to realistically represent university campus dynamics during epidemics. We evaluate Q-learning for a discretized state space which resulted in a policy matrix that not only guides occupancy decisions under varying epidemic conditions but also illustrates the inherent trade-off in epidemic management. This trade-off is characterized by the dilemma between stricter measures, which may effectively reduce infections but impose less educational benefit (more in-person interactions), and more lenient policies, which could lead to higher infection rates.