用文本、关卡和草图构建共享表示，让游戏关卡生成更符合人类设计意图。
Human-Aligned Procedural Level Generation Reinforcement Learning via Text-Level-Sketch Shared Representation
- 三模态共享嵌入空间，通过对比学习对齐文本、关卡与草图
- 引入基于嵌入相似性的辅助奖励，提升生成结果的人类风格匹配度
- 在定量指标和人工评估中均优于现有方法，适合人机协同设计
人机对齐的AI是协同创作的关键，能准确理解人类意图并生成可控内容。在强化学习驱动的程序化内容生成（PCGRL）中，这一目标尤为重要。然而现有系统常缺乏以人为核心的行为，限制了实际应用。本文提出VIPCGRL框架，融合文本、关卡和草图三种模态，构建跨模态共享嵌入空间，并通过四重对比学习与人机风格对齐训练。采用基于嵌入相似性的辅助奖励优化策略。实验表明，该方法在量化指标和人工评估中均显著优于基线模型。代码与数据集已开源：https://github.com/bic4907/VIPCGRL。
原文摘要 · Abstract (English)
Human-aligned AI is a critical component of co-creativity, as it enables models to accurately interpret human intent and generate controllable outputs that align with design goals in collaborative content creation. This direction is especially relevant in procedural content generation via reinforcement learning (PCGRL), which is intended to serve as a tool for human designers. However, existing systems often fall short of exhibiting human-centered behavior, limiting the practical utility of AI-driven generation tools in real-world design workflows. In this paper, we propose VIPCGRL (Vision-Instruction PCGRL), a novel deep reinforcement learning framework that incorporates three modalities-text, level, and sketches-to extend control modality and enhance human-likeness. We introduce a shared embedding space trained via quadruple contrastive learning across modalities and human-AI styles, and align the policy using an auxiliary reward based on embedding similarity. Experimental results show that VIPCGRL outperforms existing baselines in human-likeness, as validated by both quantitative metrics and human evaluations. The code and dataset are available at https://github.com/bic4907/VIPCGRL.
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