Deep Reinforcement Learning for 3D Furniture Layout in Indoor Graphic Scenes

In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling. In this paper, we explore the interior graphic scenes design task as a Markov decision process (MDP) in 3D simulation, which is solved by deep reinforcement learning. The goal is to produce a proper furniture layout in the 3D simulation of the indoor graphic scenes. In particular, we first transform the 3D interior graphic scenes into two 2D simulation scenes. We then design the simulated environment and apply two reinforcement learning agents to learn the optimal 3D layout for the MDP formulation in a cooperative way. We conduct our experiments on a large-scale real-world interior layout dataset that contains industrial designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts as compared with the state-of-art model.