Solving 3D Bin Packing Problem via Multimodal Deep Reinforcement Learning

Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes or more. Experiments show that the agent could learn highly efficient policies that deliver superior performance against all the baselines on instances of various scales.

Authors

• Yuan Jiang Nanjing University
• Zhiguang Cao Singapore Management University
• Jie Zhang

Keywords

• Bin Packing Problem
• Combinatorial Optimization Problem
• Multimodal Learning
• Deep Reinforcement Learning