Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention
This work addresses a combinatorial optimization problem with potential applications in logistics and resource allocation, though it appears incremental as it builds on existing methods.
The paper tackles the bin packing problem by proposing an end-to-end deep reinforcement learning model with self-attention encoding, achieving state-of-the-art performance in both offline and online settings.
This paper seeks to tackle the bin packing problem (BPP) through a learning perspective. Building on self-attention-based encoding and deep reinforcement learning algorithms, we propose a new end-to-end learning model for this task of interest. By decomposing the combinatorial action space, as well as utilizing a new training technique denoted as prioritized oversampling, which is a general scheme to speed up on-policy learning, we achieve state-of-the-art performance in a range of experimental settings. Moreover, although the proposed approach attend2pack targets offline-BPP, we strip our method down to the strict online-BPP setting where it is also able to achieve state-of-the-art performance. With a set of ablation studies as well as comparisons against a range of previous works, we hope to offer as a valid baseline approach to this field of study.