Q-learning is a type of reinforcement learning algorithm used in machine learning. It helps an agent learn how to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, which helps it understand which actions lead to better outcomes over time. In Q-learning, the agent maintains a table called the Q-table, which stores values representing the expected future rewards for each action in a given state. By updating this table through exploration and exploitation, the agent gradually learns the best actions to take in different situations, ultimately maximizing its total reward.