  The pandemic caused by the SARS-CoV-2 virus has exposed many flaws in the
decision-making strategies used to distribute resources to combat global health
crises. In this paper, we leverage reinforcement learning and optimization to
improve upon the allocation strategies for various resources. In particular, we
consider a problem where a central controller must decide where to send testing
kits to learn about the uncertain states of the world (active learning); then,
use the new information to construct beliefs about the states and decide where
to allocate resources. We propose a general model coupled with a tunable
lookahead policy for making vaccine allocation decisions without perfect
knowledge about the state of the world. The lookahead policy is compared to a
population-based myopic policy which is more likely to be similar to the
present strategies in practice. Each vaccine allocation policy works in
conjunction with a testing kit allocation policy to perform active learning.
Our simulation results demonstrate that an optimization-based lookahead
decision making strategy will outperform the presented myopic policy.
