Deep Q-Learning

E444494
model-free reinforcement learning method off-policy learning algorithm reinforcement learning algorithm value-based reinforcement learning method

Deep Q-Learning is a reinforcement learning algorithm that uses deep neural networks to approximate Q-values, enabling agents to learn effective policies directly from high-dimensional inputs like raw images.

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Observed surface forms (4)

Surface form Occurrences
Deep Q-Network 2
DQN 1
DQN algorithm 1

Statements (47)

Predicate Object
instanceOf model-free reinforcement learning method ⓘ
off-policy learning algorithm ⓘ
reinforcement learning algorithm ⓘ
value-based reinforcement learning method ⓘ
addresses correlated training samples ⓘ
instability in Q-Learning with function approximation ⓘ
non-stationary target values ⓘ
approximates Q-values ⓘ
assumes discrete action space ⓘ
basedOn Q-Learning ⓘ
belongsTo deep reinforcement learning ⓘ
canSufferFrom overestimation bias ⓘ
enables learning from high-dimensional inputs ⓘ
learning from raw images ⓘ
estimates action-value function ⓘ
inspired Double DQN NERFINISHED ⓘ
Dueling DQN NERFINISHED ⓘ
Prioritized Experience Replay NERFINISHED ⓘ
Rainbow DQN NERFINISHED ⓘ
isImplementedIn Keras NERFINISHED ⓘ
PyTorch NERFINISHED ⓘ
TensorFlow NERFINISHED ⓘ
isNotSuitableFor large continuous action spaces without modification ⓘ
isTaughtIn deep learning courses ⓘ
reinforcement learning courses ⓘ
isUsedFor Atari 2600 game playing ⓘ
control tasks ⓘ
robotics tasks ⓘ
learns policy implicitly via Q-function ⓘ
maps states to action values ⓘ
optimizes expected cumulative reward ⓘ
requires interaction with environment ⓘ
reward signal ⓘ
typicallyUses convolutional neural networks ⓘ
updates neural network parameters ⓘ
uses Bellman equation NERFINISHED ⓘ
deep neural networks ⓘ
epsilon-greedy exploration ⓘ
experience replay ⓘ
function approximation ⓘ
replay buffer ⓘ
stochastic gradient descent ⓘ
target network ⓘ
wasDescribedIn Playing Atari with Deep Reinforcement Learning NERFINISHED ⓘ
wasExtendedIn Human-level control through deep reinforcement learning NERFINISHED ⓘ
wasIntroducedIn 2013 ⓘ
wasPopularizedBy DeepMind NERFINISHED ⓘ

Referenced by (6)

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

Hindsight Experience Replay → compatibleWith → Deep Q-Learning ⓘ
Double DQN → extends → Deep Q-Learning ⓘ
this entity surface form: Deep Q-Network
Volodymyr Mnih → knownFor → Deep Q-Learning ⓘ
this entity surface form: DQN algorithm
Alex Graves → notableWork → Deep Q-Learning ⓘ
this entity surface form: Deep Recurrent Q-Learning
Rainbow DQN → basedOn → Deep Q-Learning ⓘ
this entity surface form: Deep Q-Network
Rainbow DQN → improvesOver → Deep Q-Learning ⓘ
this entity surface form: DQN