This chapter introduces the fascinating world of bandit problems, a cornerstone of reinforcement learning. We explore the fundamental concept of the exploration-exploitation trade-off and delve into various bandit algorithms. From the classic multi-armed bandit to the more sophisticated contextual bandit, we examine how these algorithms balance learning and earning in uncertain environments. Through accessible examples and mathematical formulations, readers will gain a solid understanding of bandit problems and their wide-ranging applications in real-world scenarios.