OID-PPO: Optimal Interior Design using Proximal Policy Optimization by Transforming Design Guidelines into Reward Functions

TL;DR

This paper introduces OID-PPO, a reinforcement learning framework that transforms interior design guidelines into reward functions, enabling efficient and high-quality furniture placement in residential spaces.

Contribution

The paper presents a novel RL method that incorporates expert design principles into reward functions, improving layout quality and computational efficiency over existing approaches.

Findings

Outperforms state-of-the-art methods in layout quality

Demonstrates significant computational efficiency gains

Shows effective integration of design guidelines into RL

Abstract

Designing residential interiors strongly impacts occupant satisfaction but remains challenging due to unstructured spatial layouts, high computational demands, and reliance on expert knowledge. Existing methods based on optimization or deep learning are either computationally expensive or constrained by data scarcity. Reinforcement learning (RL) approaches often limit furniture placement to discrete positions and fail to incorporate design principles adequately. We propose OID-PPO, a novel RL framework for Optimal Interior Design using Proximal Policy Optimization, which integrates expert-defined functional and visual guidelines into a structured reward function. OID-PPO utilizes a diagonal Gaussian policy for continuous and flexible furniture placement, effectively exploring latent environmental dynamics under partial observability. Experiments conducted across diverse room shapes and furniture configurations demonstrate that OID-PPO significantly outperforms state-of-the-art methods in terms of layout quality and computational efficiency. Ablation studies further demonstrate the impact of structured guideline integration and reveal the distinct contributions of individual design constraints.