This 39-minute lecture from the Agentic AI Course explores Q-Learning, a fundamental Reinforcement Learning technique. Gain a comprehensive understanding of both theoretical concepts and practical implementation of Q-Learning using Python. Learn about the fundamentals of learning agents, how Q-Learning algorithms work, the Epsilon-Greedy exploration strategy, Q-Table initialization and update mechanisms, and follow along with hands-on coding examples in a simple environment. Access the accompanying code on GitHub through the provided repository link. Perfect for both beginners and those refreshing their knowledge of reinforcement learning concepts. The lecture is part of Code With Aarohi's Agentic AI Course series.