Abstract Spatial layout planning is a core issue in architectural design with a significant impact on building performance. Although various artificial intelligence methods have advanced automated spatial layout planning in the construction area, applications of reinforcement learning for existing building renovation, particularly those with performance-based aims, remain limited. This study proposes an automated spatial layout planning framework using deep reinforcement learning to evaluate its viability and efficiency in functional layout renovation targeting sustainable objectives. The agent-based simulation with the queuing theory was employed to capture the occupant patterns, providing a foundation for the performance indicators of the reinforcement learning model. The model was trained and applied to a real case to generate optimized layouts, achieving improvements in energy and occupant circulation compared to the original layout plan. It should be clarified that the above energy-optimization estimation of the model relied on a simplified approximation rather than a dynamic energy simulation. The results demonstrate the advantages of this framework in advancing AI-aided design and indicate future research directions for enhancing the design efficiency and quality in automated spatial layout planning.
Dai et al. (Wed,) studied this question.