#6309. Design Synthesis through a Markov Decision Process and Reinforcement Learning Framework

September 2026publication date
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Library and Information Sciences;
Information Systems;
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Abstract:
This article presents a framework that mathematically models optimal design synthesis as a Markov Decision Process (MDP) that is solved with reinforcement learning. In this context, the states correspond to specific design configurations, the actions correspond to the available alterations modeled after generative design grammars, and the immediate rewards are constructed to be related to the improvement in the altered configurations performance with respect to the design objective. Since in the context of optimal design synthesis the immediate rewards are in general not known at the onset of the process, reinforcement learning is employed to efficiently solve the MDP. The goal of the reinforcement learning agent is to maximize the cumulative rewards and hence synthesize the best performing or optimal design.
Keywords:
computational synthesis; design synthesis; machine learning for engineering applications; reinforcement learning; truss optimization

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