Deep reinforcement learning: Difference between revisions

Revision as of 05:31, 22 January 2024

Revision as of 03:23, 28 January 2024

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{{Short description|Machine learning that combines deep learning and reinforcement learning}}

{{Machine 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 manual engineering of the [[state space]]. Deep RL algorithms are able to take in very large inputs (e.g. every pixel rendered to the screen in a video game) and decide what actions to perform to optimize an objective (e.g. maximizing the game score). Deep reinforcement learning has been used for a diverse set of applications including but not limited to [[robotics]], [[video game]]s, [[natural language processing]], [[computer vision]],<ref>{{Cite journal |last=Le |first=Ngan |last2=Rathour |first2=Vidhiwar Singh |last3=Yamazaki |first3=Kashu |last4=Luu |first4=Khoa |last5=Savvides |first5=Marios |date=2022-04-01 |title=Deep reinforcement learning in computer vision: a comprehensive survey |url=https://doi.org/10.1007/s10462-021-10061-9 |journal=Artificial Intelligence Review |language=en |volume=55 |issue=4 |pages=2733–2819 |doi=10.1007/s10462-021-10061-9 |issn=1573-7462|arxiv=2108.11510 }}</ref>, education, transportation, finance and [[Health care|healthcare]].<ref name="francoislavet2018"/>

== Overview ==