Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning

摘要

There has been intense debate about lockdown policies in the context of Covid-19 for limiting damage both to health and to the economy. We present an Al-driven approach for generating optimal lockdown policies that control the spread of the disease while balancing both health and economic costs. Furthermore, the proposed reinforcement learn-ing approach automatically learns those policies, as a function of disease and population parameters. The approach accounts for imperfect lockdowns, can be used to explore a range of policies using tunable parameters, and can be easily extended to fine-grained lockdown strictness. The control approach can be used with any compatible disease and network simulation models.