Using Conditional GANs to Build Zelda Game Levels

In recent years AI models have learned to play games ranging from Go to Poker to StarCraft. One-by-one, the machines have demonstrated their superiority over even the strongest human players. With the challenge seemingly solved for existing games, what about testing AI in an entirely different way -- by having it design new games for humans to play? A team of researchers has proposed a Generative Adversarial Network based model tasked with creating playable and aesthetically appealing game levels for popular action-adventure video game series The Legend of Zelda. In their paper Bootstrapping Conditional GANs for Video Game Level Generation, researchers from New York University, IT University of Copenhagen, OriGen.ai, and modl.ai