Learning Local Constraints for Reinforcement-Learned Content Generators

Excerpt

arXiv:2605.13570v2 Announce Type: replace-cross Abstract: Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually satisfying game levels but face challenges in optimizing global properties, such as playability. On the other hand, reinforcement-learning-trained generators can optimize global properties---because such properties can easily be included in reward functions---but the results can be visual