Code "Large Language Model-Driven Curriculum Design for Mobile Networks" which has been accepted as a symposium paper at IEEE ICCC 2024. The preprint of the manuscript is available here: [https://arxiv.org/abs/2405.18039]. In this paper we explore using an LLM to generate a curriculum for an RL agent to train on.
To run the code in this repository, you need the following:
- Python 3.8 or higher
- Jupyter Notebook
- Required Python packages (listed in
requirements.txt
) - Modified mobile-env (https://github.com/stefanbschneider/mobile-env) github repo, plese make sure to cite their work. This can be added as a submodule.
- This repo will be updated with modified mobile-env repo soon.
-
Clone the repository:
git clone https://github.com/OmarErak/LLM-CL.git cd LLM-CL
-
Create a virtual environment:
python3 -m venv env source env/bin/activate # On Windows, use `env\Scripts\activate`
-
Install the required packages:
pip install -r requirements.txt
-
Open the Jupyter Notebook:
jupyter notebook
-
Navigate to the
curriculum_learning.ipynb
notebook and open it. -
Run the notebook cells:
- The notebook is organized into sections that correspond to different stages of the implementation.
- Follow the instructions and comments within the notebook to execute each section.
curriculum_learning.ipynb
: Jupyter notebook containing the implementation of the LLM-driven curriculum design for RL in mobile networks.one_shot_learning.ipynb
: Jupyter notebook containing the implementation of an RL agent trained on the final environment.requirements.txt
: List of required Python packages.README.md
: This file, providing an overview and usage instructions.
- Add the modified mobile-env repo as a submodule
This project is licensed under the MIT License - see the LICENSE file for details.
If you find the paper or codebase useful, please cite our paper:
@misc{erak2024large,
title={Large Language Model-Driven Curriculum Design for Mobile Networks},
author={Omar Erak and Omar Alhussein and Shimaa Naser and Nouf Alabbasi and De Mi and Sami Muhaidat},
year={2024},
eprint={2405.18039},
archivePrefix={arXiv},
}