Architecture Engineers Award

The increasing demand for old building renovation presents complex spatial, functional, and technical challenges that exceed the capacity of conventional design workflows. In response, this paper investigates the application of deep reinforcement learning (DRL) to enable automated architectural space composition under predefined built environment constraints. By integrating artificial intelligence into architectural design processes, the study positions computational intelligence as a strategic tool to improve efficiency, adaptability, and decision-making in renovation-oriented architectural practice. Reinforcement Learning Framework for Architectural Design The research establishes a dedicated reinforcement learning platform centered on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. This framework enables multiple agents to collaboratively explore spatial configurations while responding to shared environmental constraints. By framing architectural space compo...

The rapid evolution of artificial intelligence has opened new possibilities for integrating deep learning into architectural workflows, particularly in the transformation of conceptual sketches into structured 3D models. This study introduces an intelligent modeling framework that merges architectural domain knowledge with advanced computer vision systems to overcome the long-standing challenge of the 2D-to-3D domain gap. By embedding the phased, selective, and iterative characteristics of architectural design directly into the workflow, the proposed model aligns AI-driven generation with the precision and logic expected in traditional design practices. This integration forms the foundation for a more adaptive and intelligent design-to-model pipeline within architecture. Deep Learning Integration in Architectural Design The research demonstrates how deep learning tools such as Stable Diffusion, CycleGAN, and Pixel2Mesh can be strategically aligned with architectural reasoning. These mo...