Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations
arXiv:2105.12960 · doi:10.1109/TG.2022.3170730
Abstract
Generative Adversarial Networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search. Much previous work applying GANs to level generation focuses on fixed-size segments combined into a whole level, but individual segments may not fit together cohesively. In contrast, segments in human designed levels are often repeated, directly or with variation, and organized into patterns (the symmetric eagle in Level 1 of The Legend of Zelda, or repeated pipe motifs in Super Mario Bros). Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs). CPPNs define latent vector GAN inputs as a function of geometry, organizing segments output by a GAN into complete levels. However, collections of latent vectors can also be evolved directly, producing more chaotic levels. We propose a hybrid approach that evolves CPPNs first, but allows latent vectors to evolve later, combining the benefits of both approaches. These approaches are evaluated in Super Mario Bros. and The Legend of Zelda. We previously demonstrated via a Quality-Diversity algorithm that CPPNs better cover the space of possible levels than directly evolved levels. Here, we show that the hybrid approach (1) covers areas that neither of the other methods can, and (2) achieves comparable or superior QD scores.
arXiv commentJournal length extension of arXiv:2004.01703
References in corpus (6)
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Illuminating search spaces by mapping elites
- The VGLC: The Video Game Level Corpus
- CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation
- DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution
- TOAD-GAN: Coherent Style Level Generation from a Single Example