An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement
<|reference_start|>An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement: This paper presents an efficient deep reinforcement learning (DRL) framework for online 3D bin packing (3D-BPP). The 3D-BPP is an NP-hard problem significant in logistics, warehousing, and transportation, involving the optimal arrangement of objects inside a bin. Traditional heuristic algorithms often fail to address dynamic and physical constraints in real-time scenarios. We introduce a novel DRL framework that integrates a reliable physics heuristic algorithm and object rearrangement and stable placement. Our experiment show that the proposed framework achieves higher space utilization rates effectively minimizing the amount of wasted space with fewer training epochs.<|reference_end|>
@article{zhou2024an,
    title={An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing
  Combining Object Rearrangement and Stable Placement},
    author={Peiwen Zhou, Ziyan Gao, Chenghao Li, Nak Young Chong},
    journal={arXiv preprint arXiv:2408.09694},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09694},
    primaryClass={cs.RO}
}