Deep Q-Networks (DQN) are a type of reinforcement learning algorithm that combines Q-learning with deep neural networks. They enable agents to learn optimal actions in complex environments by approximating the Q-value function, which estimates the expected future rewards for each action taken in a given state. The architecture of a DQN allows it to handle high-dimensional input spaces, such as images, making it particularly effective in tasks like playing video games. By using techniques like experience replay and target networks, DQNs improve learning stability and efficiency, leading to impressive performance in various applications.