Deep Q-Networks

Deep Q-Networks (DQN) is an algorithm that combines Q-learning with deep neural networks to learn value functions over high-dimensional state spaces such as raw pixel input. Introduced by DeepMind in 2013 and published in Nature in 2015, DQN demonstrated human-level or superhuman performance on 49 Atari 2600 games using only game frames and scores as input — a landmark result establishing that deep learning could be successfully applied to sequential decision problems. Key innovations include the experience replay buffer (breaking temporal correlations in training data) and the target network (stabilizing the Bellman update target). DQN opened the modern era of deep reinforcement learning and spawned dozens of variants addressing its sample inefficiency and instability under distribution shift.