HCVEA: Personalized Residential Layout Generation via an Improved Conditional Variational Autoencoder with Reinforcement Learning

With the growing demand for personalized design, residential layout generation has become a key research area in architecture and artificial intelligence. This paper presents a novel method for generating personalized layouts using an improved Conditional Variational Autoencoder (HCVEA). This method introduces conditional variables to control key features such as room count, functional zoning, and spatial distribution, while enhancing the latent space structure to improve both diversity and controllability. By incorporating user preferences as conditional inputs and integrating reinforcement learning with the autoencoder-based architecture, the layout generation process is optimized for more accurate and efficient results. A novel Connectionist Temporal Classification Attention (CTC-Attention) decoder is introduced to improve contextual semantic understanding. The Asynchronous Advantage Actor–Critic (A3C) reinforcement learning algorithm is employed to enhance training efficiency. Experimental results averaged over three independent runs show that HCVEA achieves an FZMR of 90.7% and an RAR of 92.3% with low standard deviation, outperforming baseline models. It also maintains a constraint compliance rate of 88.6% and adaptability to different room configurations.