Daniele Fernandes E Silva
ABSTRACT
Procedural Content Generation algorithms aim to create unique and variable dungeon maps, ensuring that players encounter infinite maps in the game. This capability is essential to prevent repetitive environments, keeping players engaged and providing them with new challenges and discoveries. Machine learning techniques, such as Generative Adversarial Networks (GANs), have proven effective in generating data, although they may have specific limitations. This paper proposes a GAN-based approach for generating dungeon maps and introduces three optimizations to enhance the training process. Our approach achieves remarkable results in producing valid and varied maps compared to existing methods. We demonstrate that our approach outperforms other approaches by generating more valid maps with increased variability.
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Index Terms
- How to improve the quality of GAN-based map generators
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