Deep-Pack: A Vision-Based 2D Online Bin Packing Algorithm with Deep Reinforcement Learning

This paper looks into the problem of online 2D bin packing where the objective is to place an incoming object in a way so as to maximize the overall packing density inside the bin. Unlike off-line methods, the online methods do not make use of information about the sequence of future objects that are going to arrive and hence, are comparatively difficult to solve. A deep reinforcement learning framework based on Double DQN is proposed to solve this problem that takes an image showing the current state of the bin as input and gives out the pixel location where the incoming object needs to be placed as the output. The reward function is defined in such a way so that the system learns to place an incoming object adjacent to the already placed items so that the maximum grouped empty area is retained for future placement. The resulting approach is shown to outperform existing state-of-the-art-method for 2D online packing and can easily be extended to 3D online bin packing problems.