Reinforcement Learning: A Brief Guide

Reinforcement learning has the potential to solve tough decision-making problems in many applications, including industrial automation, autonomous driving, video game playing, and robotics. Reinforcement learning is a type of machine learning in which a computer learns to perform a task through repeated interactions with a dynamic environment. This trial-and-error learning approach enables the computer to make a series of decisions without human intervention and without being explicitly programmed to perform the task. One famous example of reinforcement learning in action is AlphaGo, the first computer program to defeat a world champion at the game of Go. Reinforcement learning works with data from a dynamic environment--in other words, with data that changes based on external conditions, such as weather or traffic flow.