Human-level control through deep reinforcement learning
The paper introduced the Deep Q-Network (DQN), which combines Q-learning with deep convolutional networks and stabilizing techniques such as experience replay and a target network. Trained end-to-end from raw pixels and game scores, a single architecture and hyperparameter set learned to play 49 Atari 2600 games. It reached or exceeded the level of a professional human games tester on the majority of titles, demonstrating a general agent learning directly from high-dimensional sensory input.