Controller synthesis is a formal method approach for automatically generating Labeled Transition System (LTS) controllers that satisfy specified properties. The efficiency of the synthesis process, however, is critically dependent on exploration policies. These policies often rely on fixed rules or strategies learned through reinforcement learning (RL) that consider only a limited set of current features. To address this limitation, this paper introduces GCRL, an approach that enhances RL-based methods by integrating Graph Neural Networks (GNNs). GCRL encodes the history of LTS exploration into a graph structure, allowing it to capture a broader, non-current-based context. In a comparative experiment against state-of-the-art methods, GCRL exhibited superior learning efficiency and generalization across four out of five benchmark domains, except one particular domain characterized by high symmetry and strictly local interactions.

翻译：控制器综合是一种形式化方法，旨在自动生成满足特定属性的标记转移系统控制器。然而，综合过程的效率关键取决于探索策略。这些策略通常依赖于固定规则或通过强化学习习得的策略，仅考虑有限的当前特征集。为克服这一局限，本文提出GCRL方法，该方法通过集成图神经网络来增强基于强化学习的方法。GCRL将LTS探索历史编码为图结构，从而能够捕获更广泛、非基于当前状态的上下文。在与最先进方法的对比实验中，除一个具有高度对称性和严格局部交互特性的特定领域外，GCRL在五个基准领域中的四个领域均展现出更优的学习效率和泛化能力。