{"id":64166,"date":"2025-09-19T13:14:46","date_gmt":"2025-09-19T07:44:46","guid":{"rendered":"https:\/\/vajiramandravi.com\/current-affairs\/?p=64166"},"modified":"2025-09-19T15:03:44","modified_gmt":"2025-09-19T09:33:44","slug":"reinforcement-learning-rl","status":"publish","type":"post","link":"https:\/\/vajiramandravi.com\/current-affairs\/reinforcement-learning-rl\/","title":{"rendered":"Reinforcement Learning (RL)"},"content":{"rendered":"<h2><b>Reinforcement Learning Latest News<\/b><\/h2>\n<p style=\"text-align: justify;\"><span style=\"font-weight: 400;\">In a paper published recently, the DeepSeek-AI team reported that their model, called just R1, could develop new forms of reasoning using reinforcement learning, a method of trial and error guided only by rewards for correct answers.<\/span><\/p>\n<h2><b>About Reinforcement Learning\u00a0<\/b><\/h2>\n<ul>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It is defined as a <\/span><b>sub-field of machine learning (ML)<\/b><span style=\"font-weight: 400;\"> that <\/span><b>enables AI-based systems<\/b><span style=\"font-weight: 400;\"> to <\/span><b>take actions in a dynamic environment<\/b><span style=\"font-weight: 400;\"> through <\/span><b>trial and error methods <\/b><span style=\"font-weight: 400;\">to <\/span><b>maximize the collective rewards based on the feedback generated for respective actions.<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In RL, an <\/span><b>autonomous agent learns<\/b><span style=\"font-weight: 400;\"> to perform a task <\/span><b>by trial and error in the absence of<\/b><span style=\"font-weight: 400;\"> any <\/span><b>guidance from a human user.<\/b><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">RL algorithms<\/span><b> use a reward-and-punishment paradigm <\/b><span style=\"font-weight: 400;\">as they process data.<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">RL is<\/span> <span style=\"font-weight: 400;\">based on the<\/span><b> hypothesis<\/b><span style=\"font-weight: 400;\"> that all <\/span><b>goals <\/b><span style=\"font-weight: 400;\">can be <\/span><b>described by<\/b><span style=\"font-weight: 400;\"> the<\/span><b> maximization of <\/b><span style=\"font-weight: 400;\">expected <\/span><b>cumulative reward.\u00a0<\/b><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The RL agent<\/span><b> learns<\/b><span style=\"font-weight: 400;\"> about a problem <\/span><b>by interacting with its environment.<\/b><span style=\"font-weight: 400;\"> The <\/span><b>environment provides information<\/b><span style=\"font-weight: 400;\"> on its current state.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The agent then <\/span><b>uses that information to determine <\/b><span style=\"font-weight: 400;\">which <\/span><b>actions(s)<\/b><span style=\"font-weight: 400;\"> to take.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><b>If that action obtains a reward<\/b><span style=\"font-weight: 400;\"> signal from the surrounding environment, the <\/span><b>agent is encouraged to take that action again <\/b><span style=\"font-weight: 400;\">when in a similar future state.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">This<\/span><b> process repeats for every new state <\/b><span style=\"font-weight: 400;\">thereafter.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><b>Over time, the agent learns from rewards and punishments<\/b><span style=\"font-weight: 400;\"> to take actions within the environment that meet a specified goal.<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The learning process in RL is<\/span><b> driven by a feedback loop <\/b><span style=\"font-weight: 400;\">that consists of four key elements:<\/span>\n<ul>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><b>Agent<\/b><span style=\"font-weight: 400;\">: The learner and decision-maker in the system.<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><b>Environment<\/b><span style=\"font-weight: 400;\">: The external world the agent interacts with.<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><b>Actions<\/b><span style=\"font-weight: 400;\">: The choices the agent can make at each step.<\/span><\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\"><b>Rewards<\/b><span style=\"font-weight: 400;\">: The feedback the agent receives after taking an action, indicating the desirability of the outcome.<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400; text-align: justify;\" aria-level=\"1\">It particularly <b style=\"font-size: inherit;\">addresses sequential decision-making problems in uncertain environments<\/b><span style=\"font-weight: 400;\"> and shows <\/span><b style=\"font-size: inherit;\">promise in artificial intelligence development<\/b><span style=\"font-weight: 400;\">.<\/span><\/li>\n<\/ul>\n<p><b>Source<\/b><span style=\"font-weight: 400;\">: <\/span><a href=\"https:\/\/epaper.thehindu.com\/ccidist-ws\/th\/th_delhi\/issues\/148695\/OPS\/G77ETROU3.1+G8AETU4UM.1.html\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">TH<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Reinforcement Learning is a sub-field of machine learning (ML) that enables AI-based systems to take actions in a dynamic environment through trial and error methods to maximize the collective rewards based on the feedback generated for respective actions. Read more about Reinforcement Learning (RL) Meaning, Features, Latest News<\/p>\n","protected":false},"author":23,"featured_media":64185,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[21,22,23],"class_list":{"0":"post-64166","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-upsc-prelims-current-affairs","8":"tag-prelims-pointers","9":"tag-upsc-current-affairs","10":"tag-upsc-prelims-current-affairs","11":"no-featured-image-padding"},"acf":[],"_links":{"self":[{"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/posts\/64166","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/comments?post=64166"}],"version-history":[{"count":0,"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/posts\/64166\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/media\/64185"}],"wp:attachment":[{"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/media?parent=64166"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/categories?post=64166"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vajiramandravi.com\/current-affairs\/wp-json\/wp\/v2\/tags?post=64166"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}