Embark on a comprehensive 9-hour Python course focused on applying Reinforcement Learning (RL) to gaming. Learn best practices for training RL models using Python and Stable Baselines 3 across four popular games: Mario, Doom, Street Fighter, and Chrome Dino. Begin with setting up environments, preprocessing data, and building RL models for each game. Progress through advanced topics like OpenAI Gym integration, hyperparameter tuning, curriculum learning, and reward shaping. Gain hands-on experience in creating custom environments, fine-tuning models, and testing RL agents. By the end of this extensive tutorial, master the skills to apply machine learning techniques to various gaming scenarios, enhancing your understanding of AI in interactive environments.