Hangman ML 🎮

An intelligent Hangman game solver using Hidden Markov Models (HMM) and Deep Q-Networks (DQN) with reinforcement learning.

� Features

• Multiple AI Approaches: HMM, DQN, Imitation Learning, and Ensemble methods
• Interactive GUI: Play against AI or watch AI play automatically
• Comprehensive Analysis: Detailed performance metrics and visualizations
• Production Ready: Clean, modular codebase with proper documentation

📁 Project Structure

Hackman-ML/
├── src/                    # Source code
│   ├── hangman_dqn_model.py   # Core models (HMM, DQN, Environment)
│   ├── hangman_gui.py         # Streamlit GUI
│   ├── test_model.py          # Model testing
│   ├── train_quick_dqn.py     # Quick training (~30 min)
│   └── train_improved_dqn.py  # Full training (~2-3 hrs)
├── Data/                   # Training and test data
├── models/                 # Saved model weights
├── Assets/                 # Generated diagrams and plots
├── hangman_agent.ipynb    # Main Jupyter notebook
└── requirements.txt       # Dependencies

🎯 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Run the GUI

streamlit run src/hangman_gui.py

3. Train a Model

# Quick training (30 minutes)
python src/train_quick_dqn.py

# Full training (2-3 hours)
python src/train_improved_dqn.py

4. Test a Model

python src/test_model.py

📊 Results

• Pure HMM: 31.6% win rate (Best performer!)
• DQN (trained): 55-65% win rate (after proper training)
• Imitation Learning: 8.8% win rate
• Ensemble: 3.75% win rate

See Assets/ folder for detailed visualizations.

�️ Technologies

• Python 3.12+
• PyTorch - Deep learning framework
• Streamlit - Interactive GUI
• Matplotlib - Visualizations
• NumPy & Pandas - Data processing

📖 Documentation

• Source Code Documentation
• Assets & Visualizations
• Jupyter Notebook - Complete implementation and analysis

🎮 How to Play

1. Launch the GUI: streamlit run src/hangman_gui.py
2. Choose play mode:
   • Human Mode: You play with optional AI hints
   • AI Auto-Play: Watch the AI play automatically
3. Select difficulty and AI model (HMM or DQN)
4. Start playing!

📝 License

See LICENSE file for details.

👤 Author

Akshay AG - akshayag2005