Reinforcement Learning · intermediate · concept 180 of 311 · Updated Nov 4, 2026
Q-Learning & Deep Q-Networks (DQN)
Q-Learning estimates the value of actions in each state. Deep Q-Networks (DQN) use neural networks to approximate Q-values, DeepMind's DQN played Atari games at superhuman level (2013). SARSA is Q-learning's on-policy sibling: it learns from the action actually taken rather than the greedy one.
Top AI contributors on this
- Demis HassabisGoogle DeepMind (chair) / Isomorphic Labs
- Pieter AbbeelAmazon / UC Berkeley
- David SilverIneffable Intelligence / UCL
- John SchulmanThinking Machines Lab
- Sergey LevineUC Berkeley / Physical Intelligence
- Chelsea FinnStanford University / Physical Intelligence
Key terms
Q-valueBellman equationExperience replayTarget networkSARSA
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Videos
▶ Large Language Models explained briefly ↗
3Blue1Brown · YouTube
Guides and articles
CS234: Reinforcement Learning ↗
Stanford
Spinning Up in Deep RL ↗
OpenAI
Reinforcement Learning: An Introduction ↗
Sutton and Barto
Introduction to Q-Learning · Hugging Face ↗
Hugging Face
Part 2: Kinds of RL Algorithms: Spinning Up documentation ↗
OpenAI Spinning Up
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