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Neural-Network-Labyrinth 🧠🔄🎮

Project Status License Python TensorFlow

Interactive maze game with neural network AI that learns to navigate labyrinths. Experience machine learning in action through gameplay!

✨ Core Features

  • Intelligent Navigation:
    • 🤖 Neural network-controlled character
    • 🧠 Real-time decision visualization
    • 🔄 On-demand training mode
  • Dynamic Environments:
    • 🌀 Procedurally generated mazes
    • 🎚️ Adjustable complexity levels
  • Game Modes:
    • 👨‍💻 Manual player control
    • 🤖 AI autonomous navigation
    • 🏁 Progress tracking with scoring
  • Immersive Experience: 🔊 Sound effects 🎨 Vector-based graphics 📊 Performance metrics

🧰 Tech Stack

  • Machine Learning: TensorFlow Keras
  • Game Engine: Pygame
  • Data Processing: NumPy

⚙️ Installation & Setup

  1. Clone repository

    git clone https://github.com/Zebaro24-Stud/Neural-Network-Labyrinth.git
cd Neural-Network-Labyrinth

  2. Create virtual environment

    python -m venv venv
# Windows
venv\Scripts\activate
# Linux/Mac
source venv/bin/activate

  3. Install dependencies

    pip install -r requirements.txt

🚀 Launching the Game

python main.py

Controls

  • Arrow Keys: Move character (Up, Down, Left, Right)

🧠 Neural Network Architecture

graph LR
    A[Input Layer<br>4 directions] --> B[Hidden Layer<br>64 neurons]
    B --> C[Output Layer<br>4 actions]
    style A fill:#4CAF50,stroke:#388E3C
    style B fill:#2196F3,stroke:#1976D2
    style C fill:#FF9800,stroke:#F57C00
Loading
  • Optimizer: Adam
  • Loss Function: Categorical Crossentropy
  • Activation: ReLU (Hidden), Softmax (Output)

🗂️ Project Structure

main.py           # Game entry point
GUI2D.py          # Graphical interface
LabyrinthLogic.py # Maze generation & logic
NeuralNetwork.py  # AI implementation
VectorGraphics.py # UI components
config.py         # Game settings
sound/            # Audio assets
requirements.txt  # Dependencies

🎮 Gameplay Preview

Manual Mode AI Mode
Manual Control AI Navigation

📬 Contacts

👨‍💻 Developer 📧 Email 💻 GitHub
Denys Shcherbatyi zebaro.work@gmail.com @Zebaro24
Dmitro Itsko cloud.me.fr@gmail.com @WhiteDh
Oleksandr Yakovenko wu662542@gmail.com @Glek-Stud