https://github.com/arash-mansourpour/reinforcement-learning_ad-optimization

AI-powered ad optimization using Reinforcement Learning and Groq API to maximize click-through rates
https://github.com/arash-mansourpour/reinforcement-learning_ad-optimization

ad-optimization adtech artificial-intelligence q-learning reinforcement-learning reinforcement-learning-agent reinforcement-learning-algorithms reinforcement-learning-environment reinforcement-learning-environments reinforcement-learning-excercises reinforcement-learning-from-human-feedback reinforcement-learning-papers reinforcement-learning-playground reinforcement-learning-tutorials tkinter

Last synced: 6 days ago
JSON representation

AI-powered ad optimization using Reinforcement Learning and Groq API to maximize click-through rates

• Host: GitHub
• URL: https://github.com/arash-mansourpour/reinforcement-learning_ad-optimization
• Owner: Arash-Mansourpour
• License: apache-2.0
• Created: 2025-05-12T14:50:15.000Z (7 days ago)
• Default Branch: main
• Last Pushed: 2025-05-12T14:57:18.000Z (7 days ago)
• Last Synced: 2025-05-12T15:57:00.626Z (7 days ago)
• Topics: ad-optimization, adtech, artificial-intelligence, q-learning, reinforcement-learning, reinforcement-learning-agent, reinforcement-learning-algorithms, reinforcement-learning-environment, reinforcement-learning-environments, reinforcement-learning-excercises, reinforcement-learning-from-human-feedback, reinforcement-learning-papers, reinforcement-learning-playground, reinforcement-learning-tutorials, tkinter
• Language: Python
• Homepage:
• Size: 16.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
Ad Optimization with RL and Groq

A smart ad optimization platform leveraging Reinforcement Learning (RL) and Groq API to maximize click-through rates (CTR). Built with a modern, dark-themed Tkinter UI, it offers intelligent ad recommendations and interactive campaign simulations.

Features

Reinforcement Learning: Uses Q-Learning to optimize ad targeting, formats, and display times.

Groq API Integration: Generates compelling ad content and strategies via AI-powered conversations.

Interactive UI: Customize campaigns and simulate performance with real-time results.

Modular Design: Easy to extend with new ad types, audiences, or business types.

Installation

Clone the repository:

git clone https://github.com/Arash-Mansourpour/Reinforcement-Learning_AD-Optimization.git

cd Reinforcement-Learning_AD-Optimization

Install dependencies:Using Poetry (recommended):

poetry install

Or with pip:

pip install -r requirements.txt

Set up Groq API Key:

Obtain a valid Groq API key from xAI API.

Set the key as an environment variable:export GROQ_API_KEY="your_groq_api_key_here"

Alternatively, update src/rlpro.py with your key (not recommended for public repositories):GROQ_API_KEY = "your_groq_api_key_here"

Usage

Run the application:

poetry run python src/rlpro.py

Or:

python src/rlpro.py

Explore the UI:

Campaign Optimization Tab: Select a business type, generate AI-driven recommendations, customize campaigns, and simulate ad performance.

Model Interaction Tab: Chat with the Groq-powered advertising assistant to create ads, get strategy suggestions, or ask questions.

Example Interactions:

Input: "Create an ad for a coffee shop"

Output: JSON with headline, body, and CTA.

Input: "Suggest ad strategies for a SaaS business"

Output: JSON with target audiences, ad types, display times, and goal.

Input: "Hi"

Output: Friendly response asking about your ad needs.

Project Structure

Reinforcement-Learning_AD-Optimization/

├── src/

│   └── rlpro.py           # Main application code

├── .gitignore             # Ignores Python cache and virtual env

├── .github/

│   └── workflows/

│       └── ci.yml         # CI/CD pipeline

├── pyproject.toml         # Poetry configuration

├── requirements.txt       # Dependency list

└── README.md              # Project documentation

Requirements

Python 3.8+

Dependencies: groq==0.9.0, numpy==1.24.0, pandas==2.0.0, customtkinter==5.2.0

A valid Groq API key

Contributing

Contributions are welcome! Please:

Fork the repository.

Create a feature branch (git checkout -b feature/your-feature).

Commit your changes (git commit -m "Add your feature").

Push to the branch (git push origin feature/your-feature).

Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Built with Groq API for AI-driven ad content.

Powered by Reinforcement Learning for intelligent ad optimization.

UI crafted with CustomTkinter for a modern look.