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=== Deep learning ===
[[File:Neural_network_example.svg|thumb|241x241px|Depiction of a basic artificial neural network]]
[[Deep learning]] is a form of [[machine learning]] that utilizes a neural network to transform a set of inputs into a set of outputs via an [[artificial neural network]]. Deep learning methods, often using [[supervised learning]] with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional raw input data (such as images) with less manual [[feature engineering]] than prior methods, enabling significant progress in several fields including [[computer vision]] and [[natural language processing]]. In the past decade, deep RL has achieved remarkable results on a range of problems, from single and multiplayer games such as [[Go (game)|Go]], [[Atari Games]], and ''[[Dota 2
=== Reinforcement learning ===
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Deep reinforcement learning reached another milestone in 2015 when [[AlphaGo]],<ref name="AlphaGo"/> a computer program trained with deep RL to play [[Go (game)|Go]], became the first computer Go program to beat a human professional Go player without handicap on a full-sized 19×19 board.
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
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.<ref name="deepmindcooling"/> Deep RL for [[autonomous driving]] is an active area of research in academia and industry.<ref name="neurips2021ml4ad"/> [[Loon_LLC|Loon]] explored deep RL for autonomously navigating their high-altitude balloons.<ref name="loonrl"/>
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