  Recent advancements in deep reinforcement learning have brought forth an
impressive display of highly skilled artificial agents capable of complex
intelligent behavior. In video games, these artificial agents are increasingly
deployed as non-playable characters (NPCs) designed to enhance the experience
of human players. However, while it has been shown that the convincing
human-like behavior of NPCs leads to increased engagement in video games, the
believability of an artificial agent's behavior is most often measured solely
by its proficiency at a given task. Recent work has hinted that proficiency
alone is not sufficient to discern human-like behavior. Motivated by this, we
build a non-parametric two-sample hypothesis test designed to compare the
behaviors of artificial agents to those of human players. We show that the
resulting $p$-value not only aligns with anonymous human judgment of human-like
behavior, but also that it can be used as a measure of similarity.
