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| title: Anti Jam | |
| emoji: 😻 | |
| colorFrom: yellow | |
| colorTo: yellow | |
| sdk: streamlit | |
| sdk_version: 1.25.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # Beyond the Anti-Jam: LLM for Zero Touch Networks | |
| [](https://github.com/yourusername/yourrepository/issues) | |
|   | |
| This project explores the integration of Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to enhance the transparency and interpretability of anti-jamming strategies in Zero Touch Networks (ZTNs). The goal is to provide human-readable explanations for DRL-based decisions, making complex strategies intuitive for network administrators. The project leverages LLMs to generate natural language descriptions for DRL actions based on observed state vectors and rewards. | |
| ## Getting Started | |
| Follow these instructions to set up and run the project on your local machine for development and testing. | |
| ### Prerequisites | |
| - Python 3.7 or higher | |
| - PyTorch | |
| - OpenAI Gym | |
| - Matplotlib | |
| - Numpy | |
| - Pandas | |
| - StreamLit | |
| For specific library versions, please refer to the `requirements.txt` file. | |
| ### Installation | |
| 1. Clone the repository to your local machine. | |
| 2. Install the required packages using pip: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Execute the script: | |
| ```bash | |
| python3 app.py | |
| ``` | |
| ### Usage | |
| The primary script trains different DQN agent variants for a specified number of episodes. After training, the agent's performance is evaluated and plotted. Relevant data, such as agent behavior, rewards, throughput, and channel switching times, are saved for further analysis. | |
| #### Repository Structure | |
| The structure of the repository is designed to maintain clarity and organization: | |
| - **agents**: This directory contains various agent implementations, categorized into different types such as actor-critic, DQN, policy gradient, and stochastic policy search agents. | |
| - **environments**: The directory houses the implementation of the RFSpectrum environment, where the agent operates and learns. | |
| - **results**: This directory stores the data and graphs generated during training and evaluation. The `Anti_Jam.py` script is the main entry point for running the training and evaluation process. | |
| - **tests**: This directory can be used to write and execute tests for the codebase. | |
| - **utilities**: The directory contains utility files, including data structures and visual assets. | |
| #### License | |
| This project is licensed under the MIT License - see the LICENSE.md file for details. | |
| #### Acknowledgements | |
| This project is supported by the following: | |
| - [Deep Reinforcement Learning Algorithms with PyTorch](https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch): This repository provides PyTorch implementations of deep reinforcement learning algorithms and environments. | |
| - **Research Paper**: The implementation is based on the research paper titled "Beyond the Anti-Jam: Unraveling DRL-based Anti-Jamming Strategy in Zero Touch Networks through Large Language Models". The paper serves as the theoretical foundation for the project and can be accessed [here](https://arxiv.org/abs/2307.06796). | |
| - **Hugging Face Transformers Library**: This repository provides tools for integrating and fine-tuning large language models, enabling natural language understanding and generation. | |
| #### Contributing | |
| Contributions to this project are welcome! If you'd like to contribute, please follow these steps: | |
| 1. Fork the repository. | |
| 2. Create a new branch for your feature/fix. | |
| 3. Make your changes and commit them with clear messages. | |
| 4. Push your changes to your forked repository. | |
| 5. Submit a pull request, detailing the changes you made and why they should be merged. | |
| Let's work together to improve this project and make it even more effective in countering jamming attacks! | |