  The year 2020 has seen the COVID-19 virus lead to one of the worst global
pandemics in history. As a result, governments around the world are faced with
the challenge of protecting 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 learning (RL) can be used to optimize mitigation policies that
minimize the economic impact without overwhelming the hospital 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
specific locations in a community; and (2) an RL-based methodology for
optimizing fine-grained mitigation policies within this simulator. Our results
validate both the overall simulator behavior and the learned policies under
realistic conditions.
