Interactive projects that demonstrate Reinforcement Learning (Q-Learning) concepts using Python and JavaScript.

These projects focus on learning by visual feedback, where an AI agent improves its decisions over time based on rewards.

An interactive football penalty shootout where:

• You choose the shot direction
• An AI goalkeeper learns using Q-Learning
• The goalkeeper adapts after every attempt

Tech Stack

• Python
• Streamlit
• NumPy
• Reinforcement Learning (Q-Learning)

Concepts Covered

• State–Action pairs
• Reward & penalty system
• Exploration vs exploitation
• Q-table updates

A neon-themed browser-based game where:

• The player shoots left, center, or right
• The AI goalkeeper learns player behavior
• Q-values are updated and visualized in real time

Tech Stack

• HTML5 Canvas
• Vanilla JavaScript
• CSS (Neon UI)
• Reinforcement Learning (Q-Learning)

Concepts Covered

• Epsilon-greedy strategy
• Learning rate & discount factor
• Real-time Q-table visualization

Both projects use the Q-Learning update rule:

Q(s, a) ← Q(s, a) + α [ r + γ max Q(s', a') − Q(s, a) ]

Where:

• α = Learning rate
• γ = Discount factor
• r = Reward

reinforcement-learning-projects/
│
├── RL Using Python/
│   ├── app.py
│   └── assets/
│
├── RL Using Js/
│   ├── index.html
│   ├── style.css
│   └── scripts.js
│
└── README.md

pip install streamlit numpy
streamlit run app.py

Then open:

http://localhost:8501

Simply open index.html in your browser.
No server or build tools required.

• Clear demonstration of Reinforcement Learning concepts
• Visual and interactive learning
• Beginner-friendly implementations
• Suitable for portfolios and academic projects

• Persistent Q-table storage
• Difficulty levels
• Deep Q-Learning (DQN)
• Advanced agent strategies

If you find this project useful, consider giving it a ⭐
Fork it, explore it, and build on top of it!

Happy Learning 🚀🧠