Simple definition of Reinforcement Learning
Reinforcement Learning (RL) is a type of machine learning where an AI learns by trial and error. It makes decisions, gets feedback (called rewards or penalties), and uses that to improve over time. The AI tries to get the most rewards, like learning the best moves in a video game or the fastest way to deliver a package. Unlike supervised learning, it doesn't get the right answers upfront, it has to figure them out through experience.
How to explain Reinforcement Learning to kids
To explain Reinforcement Learning to a child, say it's a way for computers to learn by doing, like how kids learn games. The computer tries something, sees if it was good or bad, and then tries to do better next time.
Here’s how to think about it
Imagine teaching a dog tricks. Every time the dog sits when you say “sit,” you give it a treat. If it doesn't sit, it gets nothing. Over time, the dog learns to sit quickly to get the treat. Reinforcement Learning is like that, but for computers. They try actions, get rewards (or not), and learn which actions are best.