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🎮 Game Boy Recorder (when playing meets research)

Welcome to Game Boy Recorder, an emulator-based system designed not only to play classic Game Boy games, but also to record gameplay data for research purposes in artificial intelligence.

Our long-term goal is to build a dataset that can help advance AI models in understanding human decision-making, gameplay strategies, and learning patterns.


✨ What is this?

Game Boy Recorder works like a traditional emulator, but with an important addition:

  • 🕹️ Play your favorite Game Boy games
  • 📊 Automatically record gameplay data
  • 🤖 Contribute to AI research

The system captures anonymized interaction data that can later be used to study how humans play, adapt, and solve problems.


🚀 Installation

⚠️ Note: A standalone app version is coming soon. For now, installation is available via Conda only.

1. Clone the repository

git clone https://github.com/edofazza/gameboylearning-human.git
cd game-boy-recorder

2. Create the Conda environment

conda create -n gb-recorder python=3.10
conda activate gb-recorder

3. Install dependencies

pip install -r requirements.txt

4. Run the emulator

cd src
python app.py

📦 Data Collection

While you play, the system records gameplay data such as:

  • Inputs (button presses)
  • Game states
  • Timing and sequences

🔒 Privacy first: No personal or identifying information is collected.


📤 How to Share Your Data

If you'd like to contribute to the research effort, you can send your recorded data files to:

📧 edoardo.fazzari@mbzuai.ac.ae

We deeply appreciate any contribution — every dataset helps improve the quality and impact of the research.


🤝 Contributing

Contributions are welcome!

  • Open issues for bugs or suggestions
  • Submit pull requests for improvements
  • Share ideas for research directions
  • Contributors in data collection can be featured in the repo (if they are willing)

🌍 Vision

Game Boy Recorder is an open initiative aimed at bridging gaming and artificial intelligence research. By combining human gameplay with modern data analysis, we hope to:

  • Better understand learning behavior
  • Improve AI training methods
  • Explore new approaches to game-playing agents

📌 Roadmap

  • Standalone desktop application
  • Improved data export formats
  • Public dataset release

⭐ Support

If you find this project interesting, consider giving it a star ⭐ on GitHub and sharing it with others!


💡 Contributors

  • Your name could be here!

If you contribute, feel free to add your name via a pull request or let us know so we can include you.


Happy gaming and thank you for contributing to AI research! 🚀

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