Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.

翻译：桁架构件布局设计，即在满足所有物理约束条件下寻找轻量化桁架布局，是建筑行业的基础问题。生成最优布局是一个极具挑战性的组合优化问题，通过穷举搜索求解的代价极为高昂。直接应用端到端强化学习（RL）方法同样不可行，因为在整个布局空间中仅有极小部分满足物理约束条件，这导致RL训练面临极其稀疏的奖励信号。本文提出AutoTruss框架，采用两阶段流水线高效生成兼具轻量性与有效性的桁架布局。该框架首先利用蒙特卡洛树搜索探索多样化的有效布局集合，随后通过强化学习迭代优化有效解。我们在2D和3D场景的经典桁架布局设计测试案例中开展实验与消融研究。在最具挑战性的三维测试案例中，AutoTruss将最佳已知布局的轻量化程度提升25.1%，成为桁架布局设计领域首个有效的深度强化学习方法。