A Context-Conditioned Reinforcement Learning Framework for Space Frame Structure Optimization

With the rapid advancement of artificial intelligence in recent years, its adoption in structural design has been increasing. However, supervised learning in engineering design is often limited by the availability and quality of labeled data. To address this issue, this study proposes a context-conditioned deep reinforcement learning (DRL) method for the automated optimization of space frame structures, termed the Space Frame Optimization Agent (SFO-Agent). Instead of learning a policy for a single fixed design task, the proposed agent is conditioned on task-related context variables and trained through direct interaction with a finite element analysis environment, thereby avoiding reliance on pre-collected training datasets. In addition, a piecewise reward function is further formulated using code-prescribed limit values to achieve both structural safety and economic efficiency. Numerical experiments demonstrate that the proposed agent effectively captures reusable design policies under varying task contexts and produces high-quality, code-compliant designs across the full parameter domain. Comparisons with conventional optimization algorithms further demonstrate its superior efficiency and competitive optimization performance, indicating strong potential for broad engineering applications.