Reinforcement Learning

The last part of ML is:

Reinforcement Learning

Reinforcement Learning is a type of AI learning where the computer learns by trying things out and learning from its mistakes — just like how people learn from experience. In this kind of learning, the AI is not given the right answers. Instead, it makes decisions, gets feedback, and then improves over time. When it makes a good choice, it gets a reward, and when it makes a wrong choice, it gets a penalty. For example, imagine teaching a robot to play a game. At first, it doesn’t know what to do, so it tries different moves. If it wins points, that’s a reward, and if it loses points, that’s a penalty. Over time, the robot learns which actions help it win more often. Reinforcement learning is used in many real-life situations. It helps self-driving cars learn how to drive safely, robots learn how to walk or pick up objects, and even video game AIs learn how to play better. This type of learning is powerful because it teaches the AI how to make smart decisions step by step, just like humans do through practice and experience.