  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 graphics scenes design task as a Markov decision
process (MDP) in 3D simulation, which is solved by multi-agent reinforcement
learning. The goal is to produce furniture layout in the 3D simulation of the
indoor graphics scenes. In particular, we firstly transform the 3D interior
graphic scenes into two 2D simulated 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. The developed simulator and codes are available at
\url{https://github.com/CODE-SUBMIT/simulator2}.
