Multi-armed bandits are a class of machine learning algorithms used in the field of reinforcement learning and decision-making. The name comes from the concept of a gambler pulling levers on multiple slot machines ("one-armed bandits") in a casino. Each lever represents an action or decision that the algorithm can make, and each action has an associated reward or payoff. The goal of multi-armed bandit algorithms is to balance the exploration of different actions to learn their rewards, with the exploitation of actions that have shown to yield high rewards in the past. This trade-off is known as the exploration-exploitation dilemma. Multi-armed bandit algorithms are used in a wide range of applications, from online advertising and clinical trials to recommendation systems and autonomous vehicles. They are particularly useful in scenarios where decisions need to be made in real-time, with limited information and uncertainty about the outcomes of different actions.