Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866911586921218048 |
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| author | Han, Jie Li, Tong Xu, Qingyang Song, Yong Pang, Bao Yuan, Xianfeng |
| author_facet | Han, Jie Li, Tong Xu, Qingyang Song, Yong Pang, Bao Yuan, Xianfeng |
| contents | The online 3D bin packing problem is important in logistics, warehousing and intelligent manufacturing, with solutions shifting to deep reinforcement learning (DRL) which faces challenges like low sample efficiency. This paper proposes a diffusion reinforcement learning-based algorithm, using a Markov decision chain for packing modeling, height map-based state representation and a diffusion model-based actor network. Experiments show it significantly improves the average number of packed items compared to state-of-the-art DRL methods, with excellent application potential in complex online scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10953 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization Han, Jie Li, Tong Xu, Qingyang Song, Yong Pang, Bao Yuan, Xianfeng Robotics The online 3D bin packing problem is important in logistics, warehousing and intelligent manufacturing, with solutions shifting to deep reinforcement learning (DRL) which faces challenges like low sample efficiency. This paper proposes a diffusion reinforcement learning-based algorithm, using a Markov decision chain for packing modeling, height map-based state representation and a diffusion model-based actor network. Experiments show it significantly improves the average number of packed items compared to state-of-the-art DRL methods, with excellent application potential in complex online scenarios. |
| title | Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.10953 |