Authors: Hamsa Batani, Kimon Drakopoulos, and Vishal Gupta
Reinforcement learning is a promising solution to sequential decision-making problems, but its use has largely been limited to simulation environments and e-commerce. This chapter describes a large-scale deployment of reinforcement learning in Greece during the summer of 2020 to adaptively allocate scarce testing resources to incoming passengers amidst the evolving COVID-19 pandemic. Our system, nicknamed Eva, used limited demographic information and recent testing results to guide testing in order to maximize the number of asymptomatic but infected travelers identified over the course of the tourist season. Results from the field evaluation show a marked improvement over other “openloop” testing strategies and highlight some of the challenges of deploying reinforcement learning in real-world, high-stakes settings.