Clock-RL: Self-tuning Alarm Clock

A smart alarm clock application that uses reinforcement learning (Q-learning) to adjust alarm times based on your wake-up patterns.

🚀 Features

• Self-tuning alarms: The app learns from your behavior and adjusts alarm times automatically
• Visual adjustments: See how much the algorithm has adjusted your alarms compared to your original settings
• Snooze management: Configure snooze intervals and limits for each alarm
• Learning dashboard: View statistics about your wake-up patterns and how the system is learning
• Beautiful UI: Modern, responsive interface with smooth animations

🧠 How It Works

1. Set up alarms with your desired wake-up times
2. When an alarm rings, dismiss it or snooze
3. The app records your actual wake-up time and uses it to calculate rewards
4. The Q-learning algorithm adjusts future alarm times based on your patterns
5. Over time, the system learns your optimal wake-up schedule

🔧 Technologies Used

• React
• TypeScript
• Q-learning for reinforcement learning
• Styled Components
• Framer Motion for animations
• React Icons

📋 Project Structure

clock-rl/
├── public/               # Static files
├── src/
│   ├── components/       # React components
│   ├── hooks/            # Custom React hooks
│   ├── context/          # React context providers
│   ├── algorithms/       # Q-learning implementation
│   ├── styles/           # Global styles
│   ├── types/            # TypeScript type definitions
│   ├── utils/            # Utility functions
│   └── App.tsx           # Main app component
├── docs/                 # Project documentation
└── package.json          # Dependencies and scripts

🚀 Installation and Setup

# Clone the repository
git clone https://github.com/Shafwansafi06/Clock-RL.git

# Navigate to the project directory
cd Clock-RL

# Install dependencies
npm install

# Start the development server
npm start

📊 Learning Algorithm

Clock-RL uses Q-learning, a type of reinforcement learning algorithm that learns to make decisions by trying different actions and evaluating their outcomes. The system:

• Learns from your wake-up behavior
• Adjusts alarms earlier or later as needed
• Considers factors like snooze frequency
• Optimizes for timely wake-ups

🔜 Future Improvements

• Sound customization for alarms
• Sleep quality tracking integration
• Dark/light theme options
• Multiple profiles for different users
• Weekly and monthly sleep pattern reports

📄 License

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