L-11 Learning Agents using Q-Learning (Theory and code) | Agentic AI Course

In this video, we break down Q-Learning, one of the most important algorithms in Reinforcement Learning (RL). Whether you’re a beginner in machine learning or revisiting the topic, this lecture will guide you through both theory and coding Q-Learning from scratch using Python. We’ll cover: ✅ What is a Learning Agent? ✅ Fundamentals of Q-Learning and how it works ✅ The Epsilon-Greedy strategy ✅ How to initialize and update a Q-Table ✅ Step-by-step Python implementation of Q-Learning in a simple environment 📥 Download the complete source code: 🔗 GitHub: https://github.com/codewithaarohi/Agentic-AI-Course/tree/main/Learning_agent 🌐 Website: https://codewithaarohi.ai/downloads/agentic_ai/ Both links contain the exact code used in this tutorial. 📩 For collaborations, sponsorships, or inquiries: aarohisingla1987@gmail.com 🔍 What You’ll Learn: 1- What is a Learning Agent? 2- Basics of Q-Learning and how it works 3- Epsilon-Greedy strategy explained 4- Q-Table initialization and updates 5- Coding Q-Learning from scratch with a simple environment