{"id":1242,"date":"2025-08-30T16:16:21","date_gmt":"2025-08-30T16:16:21","guid":{"rendered":"https:\/\/iothook.com\/?p=1242"},"modified":"2025-08-30T16:17:05","modified_gmt":"2025-08-30T16:17:05","slug":"reinforcement-learning-rl","status":"publish","type":"post","link":"https:\/\/iothook.com\/tr\/reinforcement-learning-rl\/","title":{"rendered":"Takviyeli \u00d6\u011frenme (RL)"},"content":{"rendered":"<p class=\"wp-block-paragraph\"><strong>Takviyeli \u00d6\u011frenme (RL)<\/strong> Yapay zek\u00e2n\u0131n bir t\u00fcr \u00f6\u011frenme y\u00f6ntemi olup, bir ajan\u0131n bir ortamda eylemler ger\u00e7ekle\u015ftirerek ve birikimli \u00f6d\u00fcl\u00fc en \u00fcst d\u00fczeye \u00e7\u0131karmak i\u00e7in kararlar almay\u0131 \u00f6\u011frenmesini i\u00e7erir. Genellikle oyunlarda yapay zek\u00e2 e\u011fitimi veya karma\u015f\u0131k sistemlerin kontrol\u00fc i\u00e7in kullan\u0131l\u0131r.<\/p>","protected":false},"excerpt":{"rendered":"<p>Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. It&#8217;s often used for training AI in games or for controlling complex systems.<\/p>","protected":false},"author":4,"featured_media":1243,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[89,97],"tags":[],"class_list":["post-1242","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-reinforcement-learning-rl"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.8 - aioseo.com -->\n\t<meta name=\"description\" content=\"Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. 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