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Code implementation of "Learning Efficient Online 3D Bin Packing on Packing Configuration Trees". We propose to enhance the practical applicability of online 3D Bin Packing Problem (BPP) via learning on a hierarchical packing configuration tree which makes the deep reinforcement learning (DRL) model easy to deal with practical constraints and well-performing even with continuous solution space.
| Date | Stars |
|---|---|
| 2026-07-24 | 1043 |
| 2026-07-25 | 1042 |
| 2026-07-28 | 1042 |
| 2026-07-30 | 1042 |
| 2026-07-31 | 1043 |
| 2026-08-06 | 1043 |
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Momentum
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growth rate 0.10%/day
## Introduction
> We are committed to continuously promoting the development of 3D packing technology.
>
> The following are functions we developed：
>- [x] Online packing solver [[1](https://github.com/alexfrom0815/Online-3D-BPP-DRL), [2](https://github.com/alexfrom0815/Online-3D-BPP-PCT), [3](https://github.com/alexfrom0815/IR-BPP)].
>- [x] Online packing with lookahead [[1](https://github.com/alexfrom0815/Online-3D-BPP-DRL)].
>- [x] Packing stability solution [[2](https://github.com/alexfrom0815/Online-3D-BPP-PCT)].
>- [x] Packing in continuous domain [[2](https://github.com/alexfrom0815/Online-3D-BPP-PCT)].
>- [x] Custom-constrained packing [[2](https://github.com/alexfrom0815/Online-3D-BPP-PCT)].
>- [x] Online packing with buffer [[3](https://github.com/alexfrom0815/IR-BPP)].
>- [x] Irregular shape packing [[3](https://github.com/alexfrom0815/IR-BPP)].
>- [x] Packing with physical constraints [[3](https://github.com/alexfrom0815/IR-BPP)].
>- [x] Basic tools for rendering, packing shape processing, and simulation scenarios [[4](https://github.com/alexfrom0815/Packing-Tools)].
> If you are interested in 3D packing, I strongly recommend you take a look. All kinds of questions and potential collaboration are welcome!
# Learning Efficient Online 3D Bin Packing on Packing Configuration Trees
We propose to enhance the practical applicability of online 3D bin packing problem (BPP) via learning on a hierarchical packing configuration tree which makes the deep reinforcement learning (DRL) model easy to deal with practical constraints and well-performing even with continuous solution space.
Compared to our previous work, the advantages of this repo are:
- [x] Container (bin) size and item sizes can be set arbitrarily.
- [x] Continuous online 3D-BPP is allowed and a continuous environment is provided.
- [x] Algorithms to approximate stability are provided ([see our other work](https://arxiv.org/abs/2108.13680v2)).
- [x] Better performance and the ability to account for more complex constraints.
- [x] More adequate heuristic baselines for domain development.
- [x] More stable training.
See these links for video demonstration: [YouTube](https://www.youtube.com/watch?v=duWgTskKwws), [bilibili](https://www.bilibili.com/video/BV1rU4y1R74S/?vd_source=b1e4277847248c95062cf16ab3b58e73)
If you are interested, please star this repo!
## Paper
For more details, please see our paper [Learning Efficient Online 3D Bin Packing on Packing Configuration Trees](https://openreview.net/forum?id=bfuGjlCwAq) which has been accepted at [ICLR 2022](https://iclr.cc/Conferences/2022). If this code is useful for your work, please cite our paper:
```
@inproceedings{
zhao2022learning,
title={Learning Efficient Online 3D Bin Packing on Packing Configuration Trees},
author={Hang Zhao and Yang Yu and Kai Xu},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=bfuGjlCwAq}
}
```
## Dependencies
* NumPy
* gym
* Python>=3.7
* [PyTorch](http://pytorch.org/) >=1.7
* My suggestion: Python == 3.7, gym==0.13.0, torch == 1.10, OS: Ubuntu 16.04
## Quick start
For training online 3D-BPP on setting 2 (mentioned in our paper) with our PCT method and the default arguments:
```bash
python main.py
```
The training data is generated on the fly. The training logs (tensorboard) are saved in './logs/runs'. Related file backups are saved in './logs/experiment'.
## Usage
### Data description
Describe your 3D container size and 3D item size in 'givenData.py'
```
container_size: A vector of length 3 describing the size of the container in the x, y, z dimensions.
item_size_set: A list records the size of each item. The size of each item is also described by a vector of length 3.
```
### Dataset
You can download the prepared dataset from [here](https://drive.google.com/drive/folders/1QLaLLnpVySt_Excerpt of 6,024 characters
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matched fp:0dd1df28b8f91319, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning