融合空间启发式策略的扩散强化学习方法用于在线 3D 装箱问题
Spatial Heuristic Strategy based Diffusion Reinforcement Learning for Online 3D Bin Packing
AI 导读
研究者提出一种结合空间启发式策略的扩散强化学习方法，用于在线 3D 装箱问题。该方法通过启发式动作预筛选压缩动作空间，用图神经网络建模空间结构，并设计包含平整度的复合奖励函数抑制空间碎片化。实验表明该方法融合了经典启发式先验知识与自适应优化能力，改善了对空间拓扑结构的感知与布局优化。
正文 · 原文
Abstract:Traditional heuristic methods have accumulated mature experience in core objectives, such as space utilization and layout compactness of 3D bin packing, relying on artificially designed rule-based logic. However, they exhibit obvious limitations in generalization ability and adaptability to dynamic scenarios. Diffusion reinforcement learning, which integrates the spatial search capability of reinforcement learning and the data distribution modeling capability of diffusion learning, has achieved certain progress in action distribution modeling and decision compliance constraints. Nevertheless, there remain issues including insufficient perception of the spatial topological structure in 3D bin packing and limited layout optimization capability. To address these problems, we propose a diffusion reinforcement learning method integrated with spatial heuristic strategies, which combines classical bin packing heuristic rules with a diffusion reinforcement learning framework. Specifically, it compresses the action space through heuristic action pre-screening, introduces a graph neural network to model the spatial structure, and designs a composite reward function incorporating flatness to suppress spatial fragmentation, thereby realizing the organic integration of prior knowledge and adaptive optimization. Experimental results demonstrate that the pro
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32241 [cs.AI] |
| (or arXiv:2610.32241v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32241 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qingyang Xu [view email]
[v1]
Fri, 23 Oct 2026 06:49:30 UTC (1,773 KB)
来源：arXiv cs.AI · arxiv.org