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In the industrial interior design process, professional designers plan thefurniture layout to achieve a satisfactory 3D design for selling. In thispaper, we explore the interior graphics scenes design task as a Markov decisionprocess (MDP) in 3D simulation, which is solved by multi-agent reinforcementlearning. The goal is to produce furniture layout in the 3D simulation of theindoor graphics scenes. In particular, we firstly transform the 3D interiorgraphic scenes into two 2D simulated scenes. We then design the simulatedenvironment and apply two reinforcement learning agents to learn the optimal 3Dlayout for the MDP formulation in a cooperative way. We conduct our experimentson a large-scale real-world interior layout dataset that contains industrialdesigns from professional designers. Our numerical results demonstrate that theproposed model yields higher-quality layouts as compared with the state-of-artmodel. The developed simulator and codes are available at\url{https://github.com/CODE-SUBMIT/simulator2}.

Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes