Solving Offline 3D Bin Packing Problem with Large-sized Bin via Two-stage Deep Reinforcement Learning

Existing Deep Reinforcement Learning (DRL) algorithms address the 3D Bin Packing Problem (3D-BPP) by decomposing the packing action into three sub-stages. However, this three-stage scheme makes it necessary for information to be passed between subnetworks, which may increase the computational cost of training and inference. This paper proposes a two-stage DRL algorithm, combining index and orientation into a single sub-stage to simplify learning. Additionally, a Bidirectional Cooperative Packing (BCP) method is introduced to compress the action space during position selection while retaining exploration capability. The experimental results show that the two-stage DRL algorithm, which incorporates BCP, achieves 0.3%-1.7% improvement in space utilization compared to the currently best-performing algorithm.