  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.
