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Faster sorting algorithms discovered using deep reinforcement learning | Nature

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Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Slider with three content items shown per slide. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. John Jumper, Richard Evans, … Demis Hassabis Brenden M. Lake & Marco Baroni Ethan B. Richman, Nicole Ticea, … Liqun Luo Abhishek Sharma, Dániel Czégel, … Leroy Cronin Mototaka Suzuki, Cyriel M. A. Pennartz & Jaan Aru Joseph L. Watson, David Juergens, … David Baker Oh-Hyeon Choung, Riccardo Vianello, … José Jiménez-Luna Hanchen Wang, Tianfan Fu, … Marinka Zitnik Wenkai Wang, Chenjie Feng, … Jianyi Yang Nature volume 618, pages 25

Download PDF Subjects Computer science Software Abstract Fundamental algorithms such as sorting or hashing are used trillions of times on any given day 1 . As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved in the past 2 , making further improvements on the efficiency of these routines has proved challenging for both human scientists and computational approaches. Here we show how artificial intelligence can go beyond the current state of the art by discovering hitherto unknown routines. To

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