Reinforcement Learning Basics With Examples (Markov Chain and Tree Search) - neptune.ai

Have you ever played against the computer in a video game, and wondered how it gets so good? Well, a big part of it is reinforcement learning. Reinforcement Learning (RL) is a machine learning domain that focuses on building self-improving systems that learn for their own actions and experiences in an interactive environment. In RL, the system (learner) will learn what to do and how to do based on rewards. Unlike other machine learning algorithms, we don't tell the system what to do. It autonomously explores and discovers which action can yield the most rewards. Reinforcement problems are considered a closed-loop because the system's present actions will influence its later inputs. "Reinforcement Learning, in the context of machine learning and artificial intelligence, is a type of dynamic programming that trains algorithms using a system of reward and punishment." In this article, we're going to explore reinforcement learning in-depth along with some practical examples.