Graph-based reinforcement learning for the optimal design of skeletal structures ― case 3: assembly sequence optimization of spatial trusses

We consider a truss as a graph consisting of nodes and edges, and combine graph embedding (GE) and
reinforcement learning (RL) to develop an agent for generating a stable assembly path for a truss with
arbitrary configuration. GE is a method of embedding the features of a graph into a vector space. By
using GE, the agent can obtain numerical information of neighboring members and nodes considering
their connectivity. Since the stability of a structure is strongly affected by the relative positions of
members and nodes, feature extraction by GE should be effective in considering the stability of a truss.
The proposed method not only can train agents using trusses with arbitrary connectivity but also can
apply trained agents to trusses with arbitrary connectivity, ensuring the versatility of the trained agents’
applicability. In the numerical examples, the trained agents are verified to find rational assembly
sequences for various trusses more than 1000 times faster than metaheuristic approaches. The trained
agent is further implemented as a user-friendly component compatible with 3D modeling software.


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