融合局部约束与强化学习，生成既美观又可玩的游戏关卡
Learning Local Constraints for Reinforcement-Learned Content Generators
- 用WFC学习局部规则，约束强化学习生成器的动作空间
- 生成的拼图平台关卡兼具视觉美感与可玩性，如Lode Runner
- 适合游戏关卡生成、AI设计等场景，需调参但效果稳定
基于约束的游戏内容生成器（如波函数坍缩，WFC）能生成视觉上令人满意的关卡，但难以保证全局属性（如可玩性）。而强化学习训练的生成器虽可通过奖励函数确保全局属性，但结果往往缺乏视觉吸引力。本文探索二者结合：将WFC学习到的局部约束用于限制PCGRL生成器的动作空间，使其在满足全局要求的同时遵循局部规则。通过调整输入数量与类型、随机坍缩初始状态及排除罕见模式进行实验。尽管方法对超参数敏感，最优模型仍能生成视觉满意且可玩的拼图平台关卡（如Lode Runner），实现高质量内容生成。
原文摘要 · Abstract (English)
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 guaranteeing global properties, such as playability. On the other hand, reinforcement-learning trained generators can guarantee global properties -- because such properties can easily be included in reward functions -- but the results can be visually dissatisfying. In this paper, we explore ways to combine these methods. Specifically, we constrain the action space of a PCGRL generator with constraints learned by WFC, effectively allowing the PCGRL generator to achieve global properties while forced to adhere to local constraints. To better analyze how this hybrid content generation method operates, we vary the number and type of inputs, and we test whether to randomly collapse the starting state and exclude rare patterns. While the method is sensitive to hyperparameter tuning, the best of our trained generators produce visually satisfying and playable puzzle-platform game levels -- such as Lode Runner levels -- with desired global properties.
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