Peter Stone's Selected Publications

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Reinforcement Learning for Optimization of COVID-19 Mitigation Policies

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.

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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.

BibTeX Entry

@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>},
}

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