What is MAB?

MAB stands for Multi-Armed Bandit. It is a classic reinforcement learning problem where an agent must choose between multiple actions (arms), each with an unknown reward distribution, to maximize their cumulative reward over time. It is commonly used in scenarios involving exploration-exploitation tradeoffs, such as online advertising (choosing which ads to display), clinical trials (selecting the best treatment), and recommendation systems (suggesting items to users).