  Large-scale screening for potential threats with limited resources and
capacity for screening is a problem of interest at airports, seaports, and
other ports of entry. Adversaries can observe screening procedures and arrive
at a time when there will be gaps in screening due to limited resource
capacities. To capture this game between ports and adversaries, this problem
has been previously represented as a Stackelberg game, referred to as a Threat
Screening Game (TSG). Given the significant complexity associated with solving
TSGs and uncertainty in arrivals of customers, existing work has assumed that
screenees arrive and are allocated security resources at the beginning of the
time window. In practice, screenees such as airport passengers arrive in bursts
correlated with flight time and are not bound by fixed time windows. To address
this, we propose an online threat screening model in which screening strategy
is determined adaptively as a passenger arrives while satisfying a hard bound
on acceptable risk of not screening a threat. To solve the online problem with
a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem
with constraints on the action space (hard bound on risk). We provide a novel
way to efficiently enforce linear inequality constraints on the action output
in Deep Reinforcement Learning. We show that our solution allows us to
significantly reduce screenee wait time while guaranteeing a bound on risk.
