Reinforcement Learning

Reinforcement learning is a type of machine learning where an AI learns by interacting with its environment and getting feedback in the form of rewards or penalties. It's similar to how people learn through trial and error. For example, a robot might try different ways to walk, and it learns to walk better when it gets a “reward” for not falling over.

This technique is used in areas where decision-making happens over time, like training AI to play video games, teach robots to navigate spaces, or even optimize ad placement on websites. The AI improves as it figures out which actions lead to better outcomes, learning to make smarter decisions step by step.