Improving Conditional Level Generation Using Automated Validation in Match-3 Games

Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the generated levels is unreliable. Despite this fact, only a few approaches that learn from existing data provide the users with ways of controlling the generation, simultaneously addressing the generation of unsolvable levels. This article proposes autovalidated level generation, a novel method to improve models that learn from existing level designs using difficulty statistics extracted from gameplay. In particular, we use a conditional variational autoencoder to generate layouts for match-3 levels, conditioning the model on precollected statistics, such as game mechanics like difficulty, and relevant visual features, such as size and symmetry. Our method is general enough that multiple approaches could potentially be used to generate these statistics. We quantitatively evaluate our approach by comparing it to an ablated model without difficulty conditioning. In addition, we analyze both quantitatively and qualitatively whether the style of the dataset is preserved in the generated levels. Our approach generates more valid levels than the same method without difficulty conditioning.