Deep Q-Network (DQN)

A Deep Q-Network (DQN) is a type of artificial intelligence that combines reinforcement learning with deep learning. It teaches an AI agent to make decisions by learning from its experiences in an environment. The agent uses a neural network to estimate the "Q-value," which represents the potential future rewards of its actions. By repeatedly trying different actions and adjusting its predictions based on the outcomes, the DQN improves its ability to choose the best actions over time, effectively learning how to perform tasks like playing video games or navigating complex environments.