Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes
This addresses the task of automated furniture arrangement in virtual reality, which is incremental as it builds on existing reinforcement learning methods for layout generation.
The paper tackles the problem of generating furniture layouts in virtual indoor scenes by formulating it as a Markov decision process and solving it with hierarchical reinforcement learning, resulting in higher-quality layouts compared to state-of-the-art models.
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.