Research on multi-agent hierarchical reinforcement learning algorithm for solving one type of 3D bin packing problem
CHU Yang
YAN Xue-feng
ZHANG Xuan-ye
XU Yun-wen
LI De-wei
Abstract：With consideration of the complexity of the three-dimensional bin packing problem(3D-BPP)in the multi-bin semi-online scenarios,a multi-agent hierarchical reinforcement learning algorithm is proposed to improve packing efficiency and space utilization.The proposed algorithm models the problem by using a multi-agent Markov decision process(MAMDP),including three fully cooperative agents responsible for item selection,bin selection,and placement planning,respectively.A distributional learning method is introduced to enhance the stability and convergence of the algorithm.Experimental results demonstrate that the algorithm exhibits superior packing performance across various envi-ronmental configurations,significantly improving space utilization and the number of packed items.It also shows strong generalization capabilities in multi-bin and multi-item selection scenarios.Compared to traditional heuristic algorithms,the proposed method has clear advantages in dynamic decision-making and adaptive optimization,particularly demonstrating robustness when handling items with unknown size distributions.The innovation lies in the first application of a multi-agent hierarchical reinforcement learning framework to the 3D-BPP,achieving end-to-end optimization of packing decisions and providing a novel solution for complex packing scenarios.
Keywords：three-dimensional packing problemdeep reinforcement learningmulti-agent reinforcement learningcombinatorial optimization
Publication Date：2025-12-30
Online Publishing Date：2026-03-05(First online date of this platform, not the publication date of the document)
Pages：8( 2569-2576 )