  In the industrial interior design process, professional designers plan the
size and position of furniture in a room to achieve a satisfactory design for
selling. In this paper, we explore the interior scene design task as a Markov
decision process (MDP), which is solved by deep reinforcement learning. The
goal is to produce an accurate position and size of the furniture
simultaneously for the indoor layout task. In particular, we first formulate
the furniture layout task as a MDP problem by defining the state, action, and
reward function. We then design the simulated environment and train
reinforcement learning agents to produce the optimal layout for the MDP
formulation. 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/simulator1}.
