  This paper proposes a novel deep reinforcement learning algorithm to perform
automatic analysis and detection of gameplay issues in complex 3D navigation
environments. The Curiosity-Conditioned Proximal Trajectories (CCPT) method
combines curiosity and imitation learning to train agents to methodically
explore in the proximity of known trajectories derived from expert
demonstrations. We show how CCPT can explore complex environments, discover
gameplay issues and design oversights in the process, and recognize and
highlight them directly to game designers. We further demonstrate the
effectiveness of the algorithm in a novel 3D navigation environment which
reflects the complexity of modern AAA video games. Our results show a higher
level of coverage and bug discovery than baselines methods, and it hence can
provide a valuable tool for game designers to identify issues in game design
automatically.

Machine Learning;Artificial Intelligence