Reinforcement Learning complete mental map

Reinforcement Learning is technically a subfield of Machine Learning, but the moment you start working with it, it feels like a completely different discipline. The math looks familiar, the neural networks look familiar, but the thinking behind it is different. This blog is a personal reference — a place to come back to when the concepts start blurring together. Part 1: The Mindset Difference How we think in ML When we learn Machine Learning, we develop a very specific frame of mind. Someone has already collected data. That data has correct answers attached to it. Our job is to train a model that finds the pattern connecting inputs to outputs, and minimize the gap between what the model predicts and what the label says. Think of it like a student in a classroom. The teacher gives you questions and the correct answers. You study the pattern. You get tested on new questions from the same pattern. The dataset is static, the ground truth exists, and learning is fundamentally passive — you