Reinforcement Learning

Reinforcement learning is a broad area in machine learning where the machine learns to perform the next step in an environment by looking at the results of actions already performed. Reinforcement learning does not have an answer, and the learning agent decides what should be done to perform the specified task. It learns from its prior knowledge. This kind of learning involves both a reward and a penalty.

No matter the type of machine learning you're using, you'll want to be able to measure how effective your model is. You can do this using various performance metrics. You will see how these are used in later chapters in the book, but a brief overview of some of the most common ones is given here.