{"id":90206,"date":"2019-05-02T09:42:44","date_gmt":"2019-05-02T16:42:44","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2019\/05\/dqn-this-paper-published-in-nature-on-26th-february-2015"},"modified":"2019-05-02T09:42:44","modified_gmt":"2019-05-02T16:42:44","slug":"dqn-this-paper-published-in-nature-on-26th-february-2015","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2019\/05\/dqn-this-paper-published-in-nature-on-26th-february-2015","title":{"rendered":"DQN: This paper published in Nature on 26th February 2015"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/dqn.jpg\"><\/a><\/p>\n<p>This paper published in <a href=\"https:\/\/storage.googleapis.com\/deepmind-media\/dqn\/DQNNaturePaper.pdf\">Nature<\/a> on 26th February 2015, describes a DeepRL system which combines Deep Neural Networks with Reinforcement Learning at scale for the first time, and is able to master a diverse range of Atari 2600 games to superhuman level with only the raw pixels and score as inputs.<\/p>\n<p>For artificial agents to be considered truly intelligent they should excel at a wide variety of tasks that are considered challenging for humans. Until this point, it had only been possible to create individual algorithms capable of mastering a single specific domain. With our algorithm, we leveraged recent breakthroughs in training deep neural networks to show that a novel end-to-end reinforcement learning agent, termed a deep Q-network (DQN), was able to surpass the overall performance of a professional human reference player and all previous agents across a diverse range of 49 game scenarios.<\/p>\n<p><a href=\"https:\/\/deepmind.com\/research\/dqn\/\" target=\"_blank\" rel=\"noopener noreferrer\"><\/p>\n<div style=\"clear:both;\">Read more<\/div>\n<p><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper published in Nature on 26th February 2015, describes a DeepRL system which combines Deep Neural Networks with Reinforcement Learning at scale for the first time, and is able to master a diverse range of Atari 2600 games to superhuman level with only the raw pixels and score as inputs. For artificial agents to [\u2026]<\/p>\n","protected":false},"author":513,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41,6],"tags":[],"class_list":["post-90206","post","type-post","status-publish","format-standard","hentry","category-information-science","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/90206","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/513"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=90206"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/90206\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=90206"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=90206"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=90206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}