DQN, or Deep Q-Network, is a type of artificial intelligence algorithm that combines Q-learning with deep learning. It is designed to help machines learn how to make decisions by estimating the value of different actions in a given state. This approach allows the algorithm to learn from experience and improve its performance over time. The key innovation of DQN is its use of a neural network to approximate the Q-value function, which predicts the expected future rewards for each action. This enables DQN to handle complex environments, such as video games, where traditional methods struggle. By training on large amounts of data, DQN can achieve impressive results in various tasks.