Deep Q-Network

DQN is the foundational deep reinforcement learning algorithm: it uses a deep convolutional network to approximate the optimal action-value function (Q-function), enabling agents to learn complex policies directly from raw pixel input.

Deep Q-Network (DQN), pioneered by DeepMind in 2013, was the first successful fusion of deep learning and reinforcement learning. The core architecture employs a deep convolutional neural network to estimate the Q-value—the expected future reward for a state-action pair—stabilizing the classic Q-learning algorithm. It introduced two critical mechanisms: Experience Replay, which stores and samples past transitions to break data correlation, and a separate Target Network, which provides stable optimization targets. This innovation allowed a single agent to achieve human-level performance on 49 distinct Atari 2600 games, setting the benchmark for general-purpose AI agents.

https://deepmind.google/blog/deep-reinforcement-learning/