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 highly performative solutions. 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：本研究探讨了结合生成语法规则与蒙特卡洛树搜索（MCTS）优化桁架结构的方法。我们的方法能够适应渐进式施工场景中特有的中间建造阶段。相较于先前研究中的其他强化学习框架（如Q学习或深度Q学习），我们证明了该方法具有显著的鲁棒性与计算效率。这些优势源于MCTS能够策略性地探索大规模状态空间，并利用树的上置信界公式有效平衡利用与探索的权衡。我们还强调了搜索树中早期决策节点的重要性，这些节点反映了对高性能解决方案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，同时不显著影响求解质量。尽管本文聚焦于桁架优化，但我们的研究表明MCTS可成为处理其他日益复杂的工程应用的有力工具。