Atari deep Q-network

E39543
deep Q-network deep reinforcement learning algorithm model-free reinforcement learning method off-policy reinforcement learning method value-based reinforcement learning method

The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.

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All labels observed (10)

Label Occurrences
Deep Q-Network 5
DQN 4
Human-level control through deep reinforcement learning 4

How this entity was disambiguated

Statements (50)

Predicate Object
instanceOf deep Q-network ⓘ
deep reinforcement learning algorithm ⓘ
model-free reinforcement learning method ⓘ
off-policy reinforcement learning method ⓘ
value-based reinforcement learning method ⓘ
achievedPerformanceLevel human-level control on many Atari 2600 games ⓘ
actionSpace discrete actions ⓘ
basedOnAlgorithm Q-learning ⓘ
coAuthor Alex Graves ⓘ
Daan Wierstra ⓘ
David Silver ⓘ
Ioannis Antonoglou ⓘ
Koray Kavukcuoglu ⓘ
Martin Riedmiller ⓘ
developedBy DeepMind ⓘ
doesNotUse game-specific prior knowledge ⓘ
hand-crafted features ⓘ
domain Atari 2600 video games ⓘ
environmentFramework Arcade Learning Environment ⓘ
evaluationMetric average score over episodes ⓘ
evaluationSetting same network architecture across games ⓘ
single set of hyperparameters across games ⓘ
firstAuthor Volodymyr Mnih ⓘ
inputFrameSize 84x84 grayscale images ⓘ
inputFrameStack 4 consecutive frames ⓘ
inputSource Atari 2600 games ⓘ
inputType raw pixel images ⓘ
inspiredAlgorithm Double DQN ⓘ
Dueling DQN ⓘ
Prioritized Experience Replay DQN ⓘ
surface form: Prioritized Experience Replay

Rainbow DQN ⓘ
introducedInPaper Atari deep Q-network self-linksurface differs ⓘ
surface form: Playing Atari with Deep Reinforcement Learning
introducedInYear 2013 ⓘ
journalPublicationYear 2015 ⓘ
learningParadigm trial-and-error learning ⓘ
notableContribution demonstrated deep learning can learn control policies directly from high-dimensional sensory input ⓘ
introduced target networks for stabilizing deep Q-learning ⓘ
popularized experience replay in deep reinforcement learning ⓘ
observationType screen images only ⓘ
outputType Q-values for discrete actions ⓘ
action-value function ⓘ
publishedInJournal Nature ⓘ
rewardSignal game score changes ⓘ
trainingSignal game score reward ⓘ
usesExplorationStrategy epsilon-greedy policy ⓘ
usesFunctionApproximator convolutional neural network ⓘ
usesLossFunction temporal-difference error ⓘ
usesOptimizationMethod stochastic gradient descent ⓘ
usesStabilizationTechnique experience replay ⓘ
target network ⓘ

How these facts were elicited

Referenced by (23)

Full triples — surface form annotated when it differs from this entity's canonical label.

DeepMind → knownFor → Atari deep Q-network ⓘ
DeepMind → developed → Atari deep Q-network ⓘ
this entity surface form: Deep Q-Network
OpenAI Baselines → implementsAlgorithm → Atari deep Q-network ⓘ
this entity surface form: Deep Q-Network
OpenAI Baselines → implementsAlgorithm → Atari deep Q-network ⓘ
this entity surface form: DQN
Atari deep Q-network → introducedInPaper → Atari deep Q-network self-linksurface differs ⓘ
this entity surface form: Playing Atari with Deep Reinforcement Learning
TF-Agents → supportsAlgorithmFamily → Atari deep Q-network ⓘ
this entity surface form: DQN
A3C → comparedWith → Atari deep Q-network ⓘ
this entity surface form: DQN
Dueling DQN → extends → Atari deep Q-network ⓘ
this entity surface form: Deep Q-Network
Dueling DQN → improvesOver → Atari deep Q-network ⓘ
this entity surface form: standard DQN
Prioritized Experience Replay DQN → basedOn → Atari deep Q-network ⓘ
this entity surface form: Deep Q-Network
DDPG → inspiredBy → Atari deep Q-network ⓘ
this entity surface form: Deep Q-Network
DDPG → contrastWith → Atari deep Q-network ⓘ
this entity surface form: DQN (which handles discrete actions)
Volodymyr Mnih → knownFor → Atari deep Q-network ⓘ
this entity surface form: human-level control through deep reinforcement learning
Volodymyr Mnih → coAuthorOf → Atari deep Q-network ⓘ
this entity surface form: Human-level control through deep reinforcement learning
Volodymyr Mnih → authorOf → Atari deep Q-network ⓘ
this entity surface form: Human-level control through deep reinforcement learning
Volodymyr Mnih → notableWork → Atari deep Q-network ⓘ
this entity surface form: Human-level control through deep reinforcement learning
Volodymyr Mnih → notableWork → Atari deep Q-network ⓘ
this entity surface form: Playing Atari with deep reinforcement learning
Volodymyr Mnih → coAuthorOf → Atari deep Q-network ⓘ
this entity surface form: Playing Atari with deep reinforcement learning
Daan Wierstra → coAuthorOf → Atari deep Q-network ⓘ
this entity surface form: “Playing Atari with Deep Reinforcement Learning”
Ioannis Antonoglou → knownFor → Atari deep Q-network ⓘ
Ioannis Antonoglou → knownFor → Atari deep Q-network ⓘ
this entity surface form: DQN
Ioannis Antonoglou → coAuthorOf → Atari deep Q-network ⓘ
this entity surface form: Playing Atari with Deep Reinforcement Learning
Ioannis Antonoglou → coAuthorOf → Atari deep Q-network ⓘ
this entity surface form: Human-level control through deep reinforcement learning