发表机构
ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合参数化图语法与安全强化学习的无数据集方法，在生成过程中直接构建满足硬约束的可行平面图嵌入，并在新基准套件上优于经典和深度生成基线。
AI 中文摘要
平面图在科学和工程应用中占据核心地位，然而现有生成器在硬性结构和几何可行性约束下对目标导向生成的支持有限。我们提出了一种无数据集的方法，通过结合参数化图语法与安全强化学习来生成平面图嵌入，在构建过程中满足约束的同时优化通用的任务特定目标。我们将生成过程建模为约束马尔可夫决策过程，其中图语法定义了状态和动作空间。我们进一步引入了一种动作投影机制，将采样动作映射到状态相关的安全集合，从而提高训练期间的约束满足度。与经典图生成器和深度生成模型（通常提供有限的目标导向控制或依赖弱约束满足）相比，我们的方法在生成过程中直接构建可行的平面图嵌入。我们还引入了一个用于约束和目标导向平面图生成的基准测试套件，以及经典和深度生成基线。在所有基准任务中，我们的方法在满足既定约束的同时始终优于基线方法。
英文摘要
Planar graphs are central to applications across science and engineering, yet existing generators provide limited support for goal-directed generation under hard structural and geometric feasibility constraints. We propose a dataset-free method for generating planar graph embeddings by combining parametric graph grammars with safe reinforcement learning to optimize generic task-specific objectives while satisfying constraints during construction. We formulate the generation process as a constrained Markov decision process, where the graph grammar defines the state and action spaces. We further introduce an action projection that maps sampled actions toward state-dependent safe sets, improving constraint satisfaction during training. In contrast to classical graph generators and deep generative models, which typically offer limited goal-directed control or rely on weak constraint satisfaction, our method constructs feasible planar graph embeddings directly during generation. We also introduce a benchmark suite for constrained and goal-directed planar graph generation, together with classical and deep generative baselines. Across all benchmark tasks, our method consistently outperforms baselines while satisfying the formulated constraints.
