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Deep reinforcement learning

Machine learning that combines deep learning and reinforcement learning

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Record originEnglish Wikipedia

Text licenseCC BY-SA 4.0

Source revisionSep 22, 2026

Entity authoritySource title only

Source-derived summary

Deep reinforcement learning 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 manual engineering of the state space. Deep RL algorithms are able to take in very large inputs and decide what actions to perform to optimize an objective. Deep reinforcement learning has been used for a diverse set of applications including but not limited to robotics, video games, natural language processing, computer vision, education, transportation, finance and healthcare.

Editorial summary

This brief starts where responsible research should: with the source description of “Deep reinforcement learning” as machine learning that combines deep learning and reinforcement learning. Everything that follows is an evidence route, not borrowed authority.

Editorial reviewA practical starting point whose main value is the path it opens into stronger specialist and primary sources. The current 108-word lead offers orientation but no explicit four-digit date, so chronology should not be assumed. The selected authority fields contribute no independent date. The account is most persuasive where Deep, reinforcement and learning can be independently traced.

Editorial analysis

Why this record matters

The subject matters to the general reference register because the source frames it as machine learning that combines deep learning and reinforcement learning. Its deeper value depends on whether names, dates, institutions and citations support that framing.

Evidence profile

Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Sep 22, 2026.

Critical limits

The absence of detail may reflect summary conventions rather than a lack of surviving documentation. The lead is largely declarative, so disagreement and counter-evidence require a deliberate search beyond the opening account. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.

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Subject index

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Deep reinforcement learning history →Deep reinforcement learning primary sources →Deep reinforcement learning archive →Deep research →reinforcement research →learning research →Machine research →

Source & attribution

This entry incorporates text from “Deep reinforcement learning” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.