# Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention

arXiv: https://arxiv.org/abs/2107.04333 · HTML: https://mlsanity.com/paper/2107.04333/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Online 3D Bin Packing with Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2006.14978/) (2020)
- [Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method](https://mlsanity.com/paper/1708.05930/) (2017)
- [Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization](https://mlsanity.com/paper/1807.01672/) (2018)
- [Self-play Learning Strategies for Resource Assignment in Open-RAN Networks](https://mlsanity.com/paper/2103.02649/) (2021)

## Built on by (most cited first, 13 of 13)

- [Planning Irregular Object Packing via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2211.09382/) (2022)
- [(X)-evolve: Solution space evolution powered by large language models](https://mlsanity.com/paper/2508.07932/) (2025)
- [Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation](https://mlsanity.com/paper/2312.08103/) (2023)
- [Deliberate Planning of 3D Bin Packing on Packing Configuration Trees](https://mlsanity.com/paper/2504.04421/) (2025)
- [Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning](https://mlsanity.com/paper/2404.04772/) (2024)
- [GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention](https://mlsanity.com/paper/2406.07579/) (2024)
- [Pattern based learning and optimisation through pricing for bin packing problem](https://mlsanity.com/paper/2409.04456/) (2024)
- [Approximately Optimal Search on a Higher-dimensional Sliding Puzzle](https://mlsanity.com/paper/2412.01937/) (2024)
- [OPA-Pack: Object-Property-Aware Robotic Bin Packing](https://mlsanity.com/paper/2505.13339/) (2025)
- [Towards VM Rescheduling Optimization Through Deep Reinforcement Learning](https://mlsanity.com/paper/2505.17359/) (2025)
- [One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem](https://mlsanity.com/paper/2510.10057/) (2025)
- [Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing](https://mlsanity.com/paper/2603.07800/) (2026)
- [Operationally Guided Placement-Aware Learning for Industrial Online 3D Bin Packing](https://mlsanity.com/paper/2607.28257/) (2026)

## Abstract (arXiv)

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.