Deep Q-Networks (DQN)

Deep Q-Networks (DQN) are a type of machine learning system that learns to make optimal decisions by combining two techniques: reinforcement learning and deep neural networks. The system interacts with an environment, like a game or a robotic task, and learns which actions lead to the best outcomes by estimating the value of each action. The neural network helps process complex data and predict these values efficiently. Over time, the DQN improves its decision-making, enabling it to perform tasks effectively without explicit programming for each step.