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。我们的方法能够适应渐进式施工场景中典型的分阶段建造过程。与先前研究中采用的Q学习或深度Q学习等强化学习框架相比，我们证明了该方法在鲁棒性和计算效率方面的显著优势。这些优势源于MCTS能够战略性地导航大规模状态空间，利用树的上置信界公式有效平衡探索-利用权衡。我们还强调了搜索树中早期决策节点的重要性，这些节点反映了对识别全局最优解至关重要的设计选择。此外，我们展示了MCTS如何动态适应复杂且庞大的状态空间，而不会显著影响解的质量。尽管本文重点聚焦于桁架优化，但我们的研究结果表明，MCTS可作为应对其他日益复杂的工程应用的有力工具。