Bandit algorithms are a class of machine learning algorithms that are used in the field of reinforcement learning, where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards. In bandit algorithms, the agent must balance the exploration of different actions to learn their potential rewards, with the exploitation of actions that have shown to be more rewarding in the past. Bandit algorithms are particularly useful in scenarios where the feedback is noisy or delayed, and where exploration is necessary to discover the best actions. These algorithms have applications in various fields such as online advertising, recommendation systems, and optimization problems. Some popular bandit algorithms include epsilon-greedy, UCB (Upper Confidence Bound), and Thompson Sampling.