In this paper, we propose and analyze a q-learning-based approach for allocation of operators to security teams in order to improve operational efficiency of an airport security checkpoint. The research is composed of two parts. First, we develop an agent-based model capable of simulating an airport security checkpoint. Second, we introduce learning agents into the model, whose goal is to allocate operators to a security team during operations, to improve operational efficiency of the security checkpoint. We propose two learning activities of these agents. Activity 1 allocates operators to the recheck process, where operators are responsible for reexamining luggages that have been rejected and decide if they are safe or not. Activity 2 allocates operators to the CT process, where operators are responsible for examining CT images of luggages and decide if a luggage should be rechecked or not. We demonstrate that introducing a learning agent with either activity increases the throughput of the security checkpoint. Furthermore, activity 1 and activity 2 decrease the time spent in critical operations for the recheck process and CT process, respectively. The behavioural strategies learned by the agents were to add an operator when there is excess luggage waiting and remove an operator when there are excess operators available. Policy evolution between two different learning agents was compared by determining the similarity in their state transition networks per episode. The similarity was computed using the DeltaCon method and proved to be a promising technique for identifying differences in agent behaviour. This study appears to be the first to dynamically-schedule security operator shifts using a reinforcement learning approach. Insights gained from this study demonstrate that dynamically allocating operators to a security lane improves its operational efficiency, which opens the possibility of dynamic-scheduling security operators for entire terminals. Furthermore, it may aid airport managers in creating more resilient security checkpoints.