Dive into the world of Reinforcement Learning through this comprehensive 3.5-hour tutorial series. Begin with an introduction to Q Learning and its table-based approach, then progress to understanding the Q Learning algorithm and agent implementation. Analyze the Q-Learning agent's performance before learning how to create a custom Reinforcement Learning (RL) environment. Advance to Deep Q Learning with DQN, and finally, master the techniques for training and testing a Deep reinforcement learning (DQN) agent. Gain practical knowledge and hands-on experience in this cutting-edge field of artificial intelligence.