A Learning-Based Approach for the Programming of Interior Layout Implementation

Given a scene layout like a room or a courtyard composed of objects, it is usually implemented manually due to the complicated state which results in a large searching space for the machine. In this paper, we propose a learning-based approach to program the implementation automatically. Our approach has two components. The main structure of our approach is the Monte Carlo Tree which searches the most valuable move for the current state. A neural network estimates the value for the leaf nodes of the searching tree. With the power of deep reinforcement learning, the network learns how to move the objects through millions of trial and error. We demonstrate our approach on different scenes and compare the performance of our approach with human performance in our experiments.