metadata

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

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

Clone the repository to your local machine.
Install the required packages using pip:
```
pip install -r requirements.txt
```
Execute the script:
```
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: 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.
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:

Fork the repository.
Create a new branch for your feature/fix.
Make your changes and commit them with clear messages.
Push your changes to your forked repository.
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!