This study investigates the combined use of generative grammar rules and Monte Carlo Tree Search (MCTS) for optimizing truss structures. Our approach accommodates intermediate construction stages characteristic of progressive construction settings. We demonstrate the significant robustness and computational efficiency of our approach compared to alternative reinforcement learning frameworks from previous research activities, such as Q-learning or deep Q-learning. These advantages stem from the ability of MCTS to strategically navigate large state spaces, leveraging the upper confidence bound for trees formula to effectively balance exploitation-exploration trade-offs. We also emphasize the importance of early decision nodes in the search tree, reflecting design choices crucial for identifying the global optimum. Additionally, we show how MCTS dynamically adapts to complex and extensive state spaces without significantly affecting solution quality. While the focus of this paper is on truss optimization, our findings suggest MCTS as a powerful tool for addressing other increasingly complex engineering applications.

翻译：本研究探讨了生成语法规则与蒙特卡洛树搜索（MCTS）在桁架结构优化中的联合应用。该方法能够适应渐进式施工场景中特有的中间建造阶段特征。我们通过对比以往研究中的替代强化学习框架（如Q学习或深度Q学习），证明了所提方法具有显著鲁棒性和计算效率优势。这些优势源于MCTS能够利用树形结构的上置信界公式策略性地导航大规模状态空间，从而有效平衡探索与利用之间的权衡关系。我们同时强调了搜索树中早期决策节点的重要性，这些节点体现了识别全局最优解所需的关键设计选择。此外，研究展示了MCTS如何在不显著影响解质量的前提下动态适应复杂且庞大的状态空间。尽管本文聚焦于桁架优化，但研究结果表明MCTS可作为解决其他日益复杂的工程应用的有力工具。