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In real life, the decoration of 3D indoor scenes through designing furniturelayout provides a rich experience for people. In this paper, we explore thefurniture layout task as a Markov decision process (MDP) in virtual reality,which is solved by hierarchical reinforcement learning (HRL). The goal is toproduce a proper two-furniture layout in the virtual reality of the indoorscenes. In particular, we first design a simulation environment and introducethe HRL formulation for a two-furniture layout. We then apply a hierarchicalactor-critic algorithm with curriculum learning to solve the MDP. We conductour experiments on a large-scale real-world interior layout dataset thatcontains industrial designs from professional designers. Our numerical resultsdemonstrate that the proposed model yields higher-quality layouts as comparedwith the state-of-art models.

Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes