Also known as: Error driven Learning
Error-driven learning is a learning approach in which an agent or model improves by using error feedback to adjust future actions or predictions. In reinforcement learning contexts, the feedback signal guides the agent toward behavior that reduces error or improves reward.
A sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to minimize some error feedback. It is a type of reinforcement learning.