DQN, which was introduced for the first time in the paper Human-level control through deep reinforcement learning by Mnih and others from DeepMind, is the first scalable reinforcement learning algorithm that combines Q-learning with deep neural networks. To overcome stability issues, DQN adopts two novel techniques that turned out to be essential for the balance of the algorithm.

DQN has proven itself to be the first artificial agent capable of learning in a diverse array of challenging tasks. Furthermore, it has learned how to control many tasks using only high-dimensional row pixels as input and using an end-to-end RL approach.