CoTASP

Code for "Continual Task Allocation in Meta-Policy Network via Sparse Prompting", presented in ICML 2023.

Key Dependencies

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 for the installation of Continual World.

Quick Start

python train_cotasp.py

Reproducibility

Tracked experiments on CW20 via Weights & Biases.

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, Meta World, and JaxRL