  In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype
for iOS and Android in which all agents are controlled by policy networks
trained using Deep Reinforcement Learning (DRL). Our aim is to understand
whether recent advances in DRL can be used to develop convincing behavioral
models for non-player characters in videogames. We begin with an analysis of
requirements that such an AI system should satisfy in order to be practically
applicable in video game development, and identify the elements of the DRL
model used in the DeepCrawl prototype. The successes and limitations of
DeepCrawl are documented through a series of playability tests performed on the
final game. We believe that the techniques we propose offer insight into
innovative new avenues for the development of behaviors for non-player
characters in video games, as they offer the potential to overcome critical
issues with
