DeepMind's DQN learns to play Atari games from pixels
DeepMind combined deep convolutional networks with Q-learning (DQN) to learn Atari 2600 games directly from screen pixels; the 2015 Nature version reached human-level performance on many of 49 games.
Key facts
- arXiv 1312.5602 'Playing Atari with Deep Reinforcement Learning' (December 2013)
- Nature paper 'Human-level control through deep reinforcement learning' (February 2015)
- Same architecture and hyperparameters across all games
- Google acquired DeepMind in early 2014
What happened
DQN used experience replay and a target network to stabilize training of a deep Q-network on raw pixels and game score.
Why it matters
Launched deep reinforcement learning as a field and put DeepMind on the path to AlphaGo.
Changelog
- 2026-09-29: created
Related events
Sources (2)
- paperPlaying Atari with Deep Reinforcement Learning (arXiv)
- paperHuman-level control through deep reinforcement learning (Nature, DOI)
id: 2013-12-19-dqn-atari · updated 2026-09-29 · open in the interactive timeline