Reinforcement learning is a special way computers learn things (alongside supervised learning and unsupervised learning). A smart program, called an agent, makes choices in a changing world. Its main goal is to get the most rewards possible over time. It learns by trying things out, much like playing a game. The agent interacts directly with its environment to learn.
One big challenge is deciding what to do next. Should it try new things to learn more (exploration)? Or should it stick to what it already knows works best (exploitation)? Finding the right balance between these two is very important. The learning process does not need a full set of rules beforehand. It learns from experience, similar to how pets learn tricks.
The agent tries to find the best way to act, called a "policy." This helps it get the highest total rewards over time. This learning is great for games like chess or controlling robots. It has led to computers beating world champions in Go. Self-driving cars also use this type of learning. Agents often try random actions sometimes to find better ways to learn.
