Evaluating Navigation Behavior of Agents in Games using Non-Parametric Statistics

Abstract

Recent advancements in deep reinforcement learning have demonstrated highly skilled agents that are capable of complex behavior. In video games, such agents are increasingly deployed as non-playable characters (NPCs) to enhance the gaming experience, as convincing human-like behavior is known to increase player engagement. However, the believability of an agent’s behavior is often measured solely by its proficiency at a given task, which alone is not sufficient to discern human-likeness. In this paper, we build a non-parametric two-sample hypothesis test to compare the behaviors of NPCs to those of human players using distributions of their movement patterns. We show that the resulting p-value metric not only aligns with anonymous human judgment of human-like behavior, but it can also be used as a measure of similarity.