{"id":26828,"date":"2018-09-02T05:15:36","date_gmt":"2018-09-02T05:15:36","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=26828"},"modified":"2026-04-29T11:12:11","modified_gmt":"2026-04-29T05:42:11","slug":"reinforcement-learning-with-python","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/reinforcement-learning-with-python\/","title":{"rendered":"Reinforcement Learning With Python &#8211; AI"},"content":{"rendered":"<p><span style=\"font-weight: 400\">So far, we\u2019ve been on some kind of journey with <strong>Artificial Intelligence using Python<\/strong>. Today, we will delve a little into Reinforcement Learning with Python. Moreover, we will discuss factors, types, and examples of Reinforcement Learning . <\/span><\/p>\n<p><span style=\"font-weight: 400\">Also, we will see a comparison of Reinforcement Learning vs Supervised Learning. At last, we will see the applications of Reinforcement Learning with Python.<\/span><\/p>\n<p>So, let&#8217;s start Reinforcement Learning with Python Tutorial.<\/p>\n<h3><strong>What is Reinforcement Learning?<\/strong><\/h3>\n<p>Python makes it simple to create environments where agents can take actions and learn from the results. For example, you can make a robot learn to walk, or a game player learn to win. RL helps machines improve their behavior over time. The agent tries different things and learns what works best.<\/p>\n<p>With Python, you can easily test and improve RL models. You can see how smart the agent becomes with each round. This is used in robotics, game AI, finance, and self-driving cars. Reinforcement learning with Python is one of the most exciting areas in modern AI, showing machines how to learn like humans from trial and error.<\/p>\n<p><strong>Limitations of reinforcement learning in Python:<\/strong><\/p>\n<ul>\n<li>It takes time to find out things, which can be time-consuming and expensive.<\/li>\n<li>As it learns from its mistakes, it can make dangerous mistakes while correcting the errors.<\/li>\n<li>It often gets confused while solving difficult real-life problems.<\/li>\n<li>AI can sometimes find shortcuts to show the work is completed, avoiding doing the actual work.<\/li>\n<\/ul>\n<h3><strong>Factors of Reinforcement Learning in Python<\/strong><\/h3>\n<p><strong>The following parameters factor in Python Reinforcement Learning:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Input-<\/strong> An initial state from which the model begins<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Output-<\/strong> Multiple possible outputs<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Training-<\/strong> The model trains based on the input, returns a state, and the user decides whether to reward or punish it<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Learning-<\/strong> The model continues to learn<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Best Solution-<\/strong> The maximum reward decides that<\/span><\/li>\n<\/ul>\n<h3><strong>Types of Reinforcement Learning in Python<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">We broadly observe two kinds of reinforcement in Python:<\/span><\/p>\n<div id=\"attachment_26863\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Types-of-Reinforcement-Learning-with-Python-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-26863\" class=\"wp-image-26863 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Types-of-Reinforcement-Learning-with-Python-01.jpg\" alt=\"Reinforcement Learning With Python\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Types-of-Reinforcement-Learning-with-Python-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Types-of-Reinforcement-Learning-with-Python-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Types-of-Reinforcement-Learning-with-Python-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Types-of-Reinforcement-Learning-with-Python-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Types-of-Reinforcement-Learning-with-Python-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-26863\" class=\"wp-caption-text\">Types of Reinforcement Learning<\/p><\/div>\n<h4><strong>a. Positive Reinforcement Learning<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Whenever certain behavior sets off an event, this strengthens the behavior and reduces its infrequency. Say it affects the behavior positively. This has the following benefits:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Optimization of performance<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Sustenance of change for long<\/span><\/li>\n<\/ul>\n<p><strong>It also faces an issue:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Too much reinforcement may overload the state and lead to diminished results<\/span><\/li>\n<\/ul>\n<h4><strong>b. Negative Reinforcement Learning<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">If we stop a negative condition, this strengthens the behavior leading to it. This is negative reinforcement and it has the following advantages:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Provision of defiance to the minimum performance standard<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Improvement of behavior<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">And like positive reinforcement learning, this has a disadvantage too:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">There is only enough to meet the minimum expected behavior<\/span><\/li>\n<\/ul>\n<h3><strong>Reinforced Learning vs Supervised Learning<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">We mentioned in section 2 that these are two different things. Now let\u2019s see how.<\/span><\/p>\n<div id=\"attachment_26865\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Reinforced-Learning-vs-Supervised-Learning-vol-.1-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-26865\" class=\"wp-image-26865 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Reinforced-Learning-vs-Supervised-Learning-vol-.1-01.jpg\" alt=\"Reinforcement Learning With python\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Reinforced-Learning-vs-Supervised-Learning-vol-.1-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Reinforced-Learning-vs-Supervised-Learning-vol-.1-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Reinforced-Learning-vs-Supervised-Learning-vol-.1-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Reinforced-Learning-vs-Supervised-Learning-vol-.1-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/08\/Reinforced-Learning-vs-Supervised-Learning-vol-.1-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-26865\" class=\"wp-caption-text\">Reinforced Learning vs Supervised Learning<\/p><\/div>\n<h4><strong>a. Decision Making<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Reinforcement learning is concerned with making decisions sequentially. So, the output depends on the current input and the next input depends on the output of the previous input.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Supervised learning, however, involves making all decisions on the initial input.<\/span><\/p>\n<h4><strong>b. Dependency and Labels<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">Decisions depend on each other in reinforcement learning. Hence, we assign labels to sequences of dependent decisions. In supervised learning, decisions are exclusive of each other, which lets us assign labels to each decision.<\/span><\/p>\n<h4><strong>c. Examples<\/strong><\/h4>\n<p><span style=\"font-weight: 400\">An example of reinforcement learning can be a game of Chess, and for supervised learning can be object recognition. (See, that\u2019s a cat. Now, look at this picture [of another cat]. Can you guess what this is?)<\/span><\/p>\n<h3><strong>Applications of Reinforcement Learning<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">This kind of learning, as you would have guessed by now, finds use in an array of use-cases:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Robotics<\/strong>&#8211; for industrial automation<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\"><strong>Machine Learning<\/strong>&#8211; for data processing<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Creation of training systems providing custom instruction and materials according to the requirements of students<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">In situations where the system must interact with its environment to collect information about it, RL techniques do great.<\/span><\/p>\n<h3><strong>Reinforcement Learning With Python Example<\/strong><\/h3>\n<p><span style=\"font-weight: 400\">Before we bid goodbye, we think we should demonstrate a simple learning agent using Python. In the following example, we implement a cartpole using the <\/span><i><span style=\"font-weight: 400\">gym<\/span><\/i><span style=\"font-weight: 400\"> package and watch it learn to balance itself:<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; import gym\r\n&gt;&gt;&gt; env=gym.make('CartPole-v0')<\/pre>\n<p><strong>[33mWARN: gym.spaces.Box autodetected <\/strong>dtype<strong> as &lt;class &#8216;numpy.float32&#8217;&gt;. Please provide explicit dtype.[0m<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; env.reset()<\/pre>\n<p><strong>array([ 0.00261226, -0.02941416, \u00a00.01968586, -0.0034146 ])<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; for _ in range(1000):\r\nenv.render()\r\nenv.step(env.action_space.sample())<\/pre>\n<div id=\"attachment_27068\" style=\"width: 536px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/09\/cartpole.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-27068\" class=\"wp-image-27068 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/09\/cartpole.png\" alt=\"Reinforcement Learning With Python Example\" width=\"526\" height=\"343\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/09\/cartpole.png 526w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/09\/cartpole-150x98.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/09\/cartpole-300x196.png 300w\" sizes=\"auto, (max-width: 526px) 100vw, 526px\" \/><\/a><p id=\"caption-attachment-27068\" class=\"wp-caption-text\">Reinforcement Learning With Python Example<\/p><\/div>\n<p>So, this was all in Reinforcement Learning with Python. Hope you like our explanation.<\/p>\n<h3><strong>Conclusion<\/strong><\/h3>\n<p>Hence, in this Python AI Tutorial, we discussed the meaning of Reinforcement Learning. Moreover, we saw types and factors of Reinforcement learning with Python. Also, we understood the concept of Reinforcement Learning with Python by an example.<\/p>\n<p>Furthermore, if you feel any confusion regarding Reinforcement Learning Python, ask in the comment tab.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>So far, we\u2019ve been on some kind of journey with Artificial Intelligence using Python. Today, we will delve a little into Reinforcement Learning with Python. Moreover, we will discuss factors, types, and examples of&#46;&#46;&#46;<\/p>\n","protected":false},"author":5,"featured_media":27070,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,46],"tags":[1132,3683,11483,11484,11485,11486],"class_list":["post-26828","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-python","tag-artificial-intelligence-reinforcement-learning-in-python","tag-deep-reinforcement-learning","tag-reinforcement-learning","tag-reinforcement-learning-course","tag-reinforcement-learning-example","tag-reinforcement-learning-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Reinforcement Learning With Python - AI - DataFlair<\/title>\n<meta name=\"description\" content=\"Let&#039;s delve a little into Reinforcement Learning with Python &amp; 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