3260 papers • 126 benchmarks • 313 datasets
As a classic NP-hard problem, the bin packing problem (1D-BPP) seeks for an assignment of a collection of items with various weights to bins. The optimal assignment houses all the items with the fewest bins such that the total weight of items in a bin is below the bin’s capacity. In its 3D version (3D-BPP), an item has a 3D “weight” corresponding to its length, width and height.
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A reward function is introduced that dictates the robot to place items in a far-to-near order and therefore simplifies the collision avoidance in movement planning of the robotic arm and outperforms the state-of-the-art methods significantly and is practically usable for real-world applications.
This research presents a novel approach to solving the three-dimensional bin-packing problem by automating the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and packing instances.
This work proposes an effective and easy-to-implement constrained deep reinforcement learning (DRL) method under the actor-critic framework and introduces a prediction-and-projection scheme that significantly outperforms the state-of-the-art methods.
This work proposes to enhance the practical applicability of online 3D-BPP via learning on a novel hierarchical representation –– packing configuration tree (PCT), a full-fledged description of the state and action space of bin packing which can support packing policy learning based on deep reinforcement learning (DRL).
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