  In real life, the decoration of 3D indoor scenes through designing furniture
layout provides a rich experience for people. In this paper, we explore the
furniture layout task as a Markov decision process (MDP) in virtual reality,
which is solved by hierarchical reinforcement learning (HRL). The goal is to
produce a proper two-furniture layout in the virtual reality of the indoor
scenes. In particular, we first design a simulation environment and introduce
the HRL formulation for a two-furniture layout. We then apply a hierarchical
actor-critic algorithm with curriculum learning to solve the MDP. 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 models.
