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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]],
== Overview ==
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In a subsequent project in 2017, [[AlphaZero]] improved performance on Go while also demonstrating they could use the same algorithm to learn to play [[chess]] and [[shogi]] at a level competitive or superior to existing computer programs for those games, and again improved in 2019 with [[MuZero]].<ref name="muzero"/> Separately, another milestone was achieved by researchers from [[Carnegie Mellon University]] in 2019 developing [[Pluribus (poker bot)|Pluribus]], a computer program to play [[poker]] that was the first to beat professionals at multiplayer games of no-limit [[Texas hold 'em]]. [[OpenAI Five]], a program for playing five-on-five [[Dota 2]] beat the previous world champions in a demonstration match in 2019.
Deep reinforcement learning has also been applied to many domains beyond games. In robotics, it has been used to let robots perform simple household tasks<ref name="levine2016"/> and solve a Rubik's cube with a robot hand.<ref name="openaihand"/><ref name="openaihandarxiv"/> Deep RL has also found sustainability applications, used to reduce energy consumption at data centers
== Algorithms ==
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