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@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={Extended version on arXiv has full details.
Simulator source code.},
}