Using Reinforcement Learning for Operating Educational Campuses Safely During a Pandemic (Student Abstract)
Abstract
The COVID-19 pandemic has brought a significant disruption not only on how schools operate but also affected student sentiments on learning and adoption to different learning strategies. We propose CampusPandemicPlanR, a reinforcement learning-based simulation tool that could be applied to suggest to campus operators how many students from each course to allow on a campus classroom each week. The tool aims to strike a balance between the conflicting goals of keeping students from getting infected, on one hand, and allowing more students to come into campus to allow them to benefit from in-person classes, on the other. Our preliminary results show that reinforcement learning is able to learn better policies over iterations, and that different Pareto-optimal tradeoffs between these conflicting goals could be obtained by varying the reward weight parameter.
Cite
Text
Ondula and Krishnamachari. "Using Reinforcement Learning for Operating Educational Campuses Safely During a Pandemic (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21649Markdown
[Ondula and Krishnamachari. "Using Reinforcement Learning for Operating Educational Campuses Safely During a Pandemic (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/ondula2022aaai-using/) doi:10.1609/AAAI.V36I11.21649BibTeX
@inproceedings{ondula2022aaai-using,
title = {{Using Reinforcement Learning for Operating Educational Campuses Safely During a Pandemic (Student Abstract)}},
author = {Ondula, Elizabeth Akinyi and Krishnamachari, Bhaskar},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {13025-13026},
doi = {10.1609/AAAI.V36I11.21649},
url = {https://mlanthology.org/aaai/2022/ondula2022aaai-using/}
}