Deep Q-Networks (DQN) are a type of artificial intelligence algorithm used in reinforcement learning. They combine traditional Q-learning with deep learning techniques, allowing the model to learn optimal actions in complex environments. DQNs use a neural network to approximate the Q-value function, which estimates the expected future rewards for each action in a given state. The DQN algorithm was popularized by researchers at DeepMind in 2015, demonstrating impressive performance in playing video games like Atari. By using experience replay and target networks, DQNs improve learning stability and efficiency, enabling the agent to make better decisions over time.