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Deep reinforcement learning

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without…

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Deep reinforcement learning

Nodes60
Edges59
Triples42
Avg. degree1.97
Density0.033333
Components1

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Deep reinforcement learning

Top relations

related to history · 12
Deep reinforcement learning → Along, Barto, Because, Bertsekas, Four, One, Seminal, Sutton, TD, TD-Gammon, Tsitiklis, With
related to Algorithms · 7
Deep reinforcement learning → At, In, Monte Carlo, Since, The, Then, Various
related to Generalization · 7
Deep reinforcement learning → Deep RL, For, One, RL, Since, The, With
related to Deep reinforcement learning · 5
Deep reinforcement learning → Deep, In, MDP, MDPs, RL
related to Research · 1
Deep reinforcement learning → Deep

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Important terminology

deep learning rl reinforcement algorithms agent neural using policy network used displaystyle learn data games computer exploration actions game research

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Goinstance offrom single and multiplayer games0.80text
Atari Gamesinstance offrom single and multiplayer games0.80text
and Dota 2 to robotics.Reinforcement learningReinforcement learning is a process in which an agent learns to make decisions through trialinstance offrom single and multiplayer games0.80text
errorinstance offrom single and multiplayer games0.80text
and Dota 2 to roboticsinstance offrom single and multiplayer games0.80text
the cross-entropy methodinstance ofThe actions selected may be optimized using Monte Carlo methods0.80text
or a combination of model-learning with model-free methods.In model-free deep reinforcement learning algorithmsinstance ofThe actions selected may be optimized using Monte Carlo methods0.80text
a policy πinstance ofThe actions selected may be optimized using Monte Carlo methods0.80text
Q-learning are better suited for off-policy learninginstance ofvalue-function based methods0.80text
have better sample-efficiency - the amount of data required to learn a task is reduced because data is re-used for learninginstance ofvalue-function based methods0.80text
Deep reinforcement learningrelated to AlgorithmsVarious0.60section
Deep reinforcement learningrelated to AlgorithmsAt0.60section

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