Automatic Truss Design with Reinforcement Learning
Weihua Du Jinglun Zhao Chao Yu Xingcheng Yao Zimeng Song Siyang Wu Ruifeng Luo Zhiyuan Liu Xianzhong Zhao Yi Wu
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.