Utilizing generative adversarial networks for stable structure generation in Angry Birds

The popular physics-based puzzle game series Angry Birds has been played and enjoyed by millions of people since its original launch in 2009. However, while the game may seem somewhat simple and straightforward to play, with even very young children being able to quickly grasp its mechanics and strategies, artificial intelligence has so far failed to obtain human-level performance. Along with a lack of knowledge about the game's internal physics engine and imprecise object detection algorithms, one of the core challenges to training better game-playing agents is the limited number and variety of available game levels. The levels in Angry Birds often contain individual structures that are made up of multiple rectangular 2D blocks, such as those shown in figure 1. While a handful of previous structure generators for Angry Birds exist, they often rely on hard-coded design constraints that limit the output diversity.