Q-Learning is a type of reinforcement learning algorithm that helps an agent learn how to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or penalties based on its actions. Over time, it learns to choose actions that maximize its total reward, creating a strategy for success. In Q-Learning, the agent maintains a table called the Q-table, which stores values representing the expected future rewards for each action in different states. By updating this table through experience, the agent gradually improves its decision-making, allowing it to navigate complex situations effectively.