• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •
Varun Kompella, Roberto Capobianco, Stacy Jong, Jonathan
Browne, Spencer Fox, Lauren Meyers, Peter Wurman, and Peter
Stone. Reinforcement Learning for Optimization of COVID-19 Mitigation Policies. In AAAI Fall Symposium on AI
for Social Good, November 2020.
Extended version on arXiv has full
details.
Simulator source code.
The year 2020 has seen the COVID -19 virus lead to one of the worst global pandemics in history. As a result, govern- ments around the world are faced with the challenge of pro- tecting 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 learn- ing (RL) can be used to optimize mitigation policies that min- imize the economic impact without overwhelming the hospi- tal 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 spe- cific locations in a community; and (2) an RL-based method- ology for optimizing fine-grained mitigation policies within this simulator. Our results validate both the overall simulator behavior and the learned policies under realistic conditions.
@InProceedings{AAAI20-symp-pandemic,
author = {Varun Kompella and Roberto Capobianco and Stacy Jong and Jonathan Browne and Spencer Fox and Lauren Meyers and Peter Wurman and Peter Stone},
title = {Reinforcement Learning for Optimization of {COVID}-19 Mitigation Policies},
booktitle = {AAAI Fall Symposium on AI for Social Good},
location = {Arlington, VA, USA},
month = {November},
year = {2020},
abstract = {
The year 2020 has seen the COVID -19 virus lead to one
of the worst global pandemics in history. As a result,
govern- ments around the world are faced with the
challenge of pro- tecting 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 learn- ing (RL) can be used to optimize
mitigation policies that min- imize the economic impact
without overwhelming the hospi- tal 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 spe-
cific locations in a community; and (2) an RL-based
method- ology for optimizing fine-grained mitigation
policies within this simulator. Our results validate
both the overall simulator behavior and the learned
policies under realistic conditions.
},
wwwnote={<a href="https://arxiv.org/abs/2010.10560">Extended version on arXiv</a> has full details.<br> Simulator <a href="https://github.com/SonyAI/PandemicSimulator">source code.</a>},
}
Generated by bib2html.pl (written by Patrick Riley ) on Mon Aug 16, 2021 07:25:55