Introduction to Multi-Armed Bandit Problems - KDnuggets

A multi-armed bandit (MAB) is a machine learning framework that uses complex algorithms to dynamically allocate resources when presented with multiple choices. In other words, it's an advanced form of A/B testing that's most commonly used by data analysts, medicine researchers, and marketing specialists. Before we delve deeper into the concept of multi-armed bandits, we need to discuss reinforcement learning, as well as the exploration vs. exploitation dilemma. Then, we can focus on various bandit solutions and practical applications. Alongside supervised and unsupervised learning, reinforcement learning is one of the basic three paradigms of machine learning. Unlike the first two archetypes we mentioned, reinforcement learning focuses on rewards and punishments for the agent whenever it interacts with the environment.