CoTASP

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Official code for the paper: Continual Task Allocation in Meta-Policy Network via Sparse Prompting

CoTASP 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# CoTASP
Code for "[**Co**ntinual **T**ask **A**llocation in Meta-Policy Network via **S**parse **P**rompting](https://arxiv.org/abs/2305.18444)", presented in ICML 2023.

## Key Dependencies
The some of dependencies are outdated, e.g., the ones listed below. You **might** need to install their latest version to run this project.
```console
python==3.7.13
- jax==0.3.17
- jaxlib==0.3.15+cuda11.cudnn82
- flax==0.6.4
- optax==0.1.4
- scikit-learn==1.0.2
- tensorflow-probability==0.18.0
- sentence-transformers==2.2.2
```
Refer to [this repo](https://github.com/awarelab/continual_world) for the installation of Continual World.

## Quick Start
```python
python train_cotasp.py
```

## Reproducibility
Tracked experiments on CW20 via [Weights & Biases](https://api.wandb.ai/links/yang-yj/5kbiuz7h).

## Citing CoTASP
If you use the code in CoTASP, please kindly cite our paper using following BibTeX entry.
```
@InProceedings{pmlr-v202-yang23t,
  title = 	 {Continual Task Allocation in Meta-Policy Network via Sparse Prompting},
  author =       {Yang, Yijun and Zhou, Tianyi and Jiang, Jing and Long, Guodong and Shi, Yuhui},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  pages = 	 {39623--39638},
  year = 	 {2023},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--29 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v202/yang23t/yang23t.pdf},
  url = 	 {https://proceedings.mlr.press/v202/yang23t.html},
}
```

## Acknowledgement
We appreciate the open source of the following projects:

[Continual World](https://github.com/awarelab/continual_world), [Meta World](https://github.com/Farama-Foundation/Metaworld), and [JaxRL](https://github.com/ikostrikov/jaxrl)

CoTASP

CoTASP 최신버젼 다운로드

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