# A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing

arXiv: https://arxiv.org/abs/2007.00463 · HTML: https://mlsanity.com/paper/2007.00463/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 11 of 11)

- [Adjustable Robust Reinforcement Learning for Online 3D Bin Packing](https://mlsanity.com/paper/2310.04323/) (2023)
- [Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation](https://mlsanity.com/paper/2312.08103/) (2023)
- [GOPT: Generalizable Online 3D Bin Packing via Transformer-based Deep Reinforcement Learning](https://mlsanity.com/paper/2409.05344/) (2024)
- [Deliberate Planning of 3D Bin Packing on Packing Configuration Trees](https://mlsanity.com/paper/2504.04421/) (2025)
- [Learning Gradient Fields for Scalable and Generalizable Irregular Packing](https://mlsanity.com/paper/2310.19814/) (2023)
- [ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization](https://mlsanity.com/paper/2501.17377/) (2025)
- [A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning](https://mlsanity.com/paper/2506.01628/) (2025)
- [Comparing Heuristics, Constraint Optimization, and Reinforcement Learning for an Industrial 2D Packing Problem](https://mlsanity.com/paper/2110.14535/) (2021)
- [Robotic Learning the Sequence of Packing Irregular Objects from Human Demonstrations](https://mlsanity.com/paper/2210.01645/) (2022)
- [OPA-Pack: Object-Property-Aware Robotic Bin Packing](https://mlsanity.com/paper/2505.13339/) (2025)
- [Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search](https://mlsanity.com/paper/2601.02649/) (2026)

## Abstract (arXiv)

We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size. The focus is on producing decisions that can be physically implemented by a robotic loading arm, a laboratory prototype used for testing the concept. The problem considered in this paper is novel in two ways. First, unlike the traditional 3D bin packing problem, we assume that the entire set of objects to be packed is not known a priori. Instead, a fixed number of upcoming objects is visible to the loading system, and they must be loaded in the order of arrival. Second, the goal is not to move objects from one point to another via a feasible path, but to find a location and orientation for each object that maximises the overall packing efficiency of the bin(s). Finally, the learnt model is designed to work with problem instances of arbitrary size without retraining. Simulation results show that the RL-based method outperforms state-of-the-art online bin packing heuristics in terms of empirical competitive ratio and volume efficiency.