Diverse level generation via machine learning of quality diversity

Abstract

Can we replicate the power of evolutionary algorithms in discovering good and diverse game content via generative machine learning (ML) techniques? This question could subvert current trends in procedural content generation (PCG) and beyond. By learning the behavior of quality-diversity (QD) evolutionary algorithms through ML, we stand to overcome the computational challenges inherent in QD search and ensure that the benefits of QD search are reproduced by efficient generative models. We introduce a novel, end-to-end methodology named Machine Learning of Quality Diversity (MLQD) which is executed in two steps. First, tailored QD evolution creates large and diverse training datasets from the ground up. Second, sophisticated ML architectures such as the Transformer learn the datasets’ underlying distributions, resulting in generative models that can emulate QD search via stochastic inference. We test MLQD on the use-case of generating strategy game map sketches, a task characterized by stringent constraints and a multidimensional feature space. Our findings are promising, demonstrating that the Transformer architecture can capture both the diversity and the quality traits of the training sets, successfully reproducing the behavior of a range of tested QD algorithms. This marks a significant advancement in our quest to automate the creation of high-quality, diverse game content, pushing the boundaries of what is possible in PCG and generative AI at large.