Human-level control through deep reinforcement learning (DQN)

The DQN paper (Nature, February 2015) is where DeepMind first demonstrated that a single deep neural network could learn to play 49 different Atari video games — directly from raw pixel inputs and game scores, with no game-specific feature engineering. The system, a deep Q-network combining convolutional neural networks with Q-learning, achieved human or superhuman performance on most games tested. The paper's significance is methodological: it proved that end-to-end deep reinforcement learning — learning a policy directly from raw sensory input without hand-crafted features — was practical. This was the first major proof point for Hassabis's thesis that general learning algorithms could substitute for domain expertise across diverse tasks. DQN's approach (experience replay, fixed Q-targets) solved the training instability that had prevented deep RL from working at scale. The paper established DeepMind's reputation in the RL community and led directly to the funding and talent that made AlphaGo possible. In retrospect, DQN's real contribution was not Atari mastery but demonstrating that the same neural network architecture could generalize across 49 different games — a first hint at the generalization that would later manifest in AlphaZero across board games and eventually in LLMs across language tasks.