{"id":183240,"date":"2024-03-29T08:57:00","date_gmt":"2024-03-29T07:57:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=183240"},"modified":"2024-03-14T21:36:20","modified_gmt":"2024-03-14T20:36:20","slug":"proximal-policy-optimization-all-about-the-algorithm-created-by-openai","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/proximal-policy-optimization-all-about-the-algorithm-created-by-openai","title":{"rendered":"Proximal Policy Optimization: all about the algorithm created by OpenAI"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"183240\" class=\"elementor elementor-183240\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c5b2fb7 e-flex e-con-boxed e-con\" data-id=\"c5b2fb7\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-00011ae elementor-widget elementor-widget-heading\" data-id=\"00011ae\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Proximal Policy Optimization is a Reinforcement Learning algorithm created by OpenAI, ideal for complex environments such as video games or robotics. Find out all you need to know about its history, how it works and how to use it!<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a91cba3 elementor-widget elementor-widget-text-editor\" data-id=\"a91cba3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>In the field of <a href=\"https:\/\/datascientest.com\/en\/kernel-everything-you-need-to-know-about-the-machine-learning-method\">Machine Learning<\/a>, <strong>Reinforcement Learning<\/strong> has been enjoying a remarkable boom over the past few years, due to its potential for solving complex problems.<\/p><p>Inspired by the human concept of learning by trial and error, this approach involves the creation of agents capable of learning through interaction with their environment to achieve specific goals.<\/p><p>These agents must develop policies, i.e. strategies, to maximize a cumulative reward over time. They perform actions and receive rewards or penalties in return, and adjust their <strong>policies to maximize the reward.<\/strong><\/p><p>However, managing to optimize these policies while maintaining learning stability represents a major challenge. To meet this challenge, OpenAI, the company behind <a href=\"https:\/\/datascientest.com\/en\/chatgpt-how-does-this-nlp-algorithm-work\">ChatGPT<\/a>, has created an innovative algorithm: PPO, or Proximal Policy Optimization.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d1bc0af elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d1bc0af\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">All about PPO<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-064dd85 e-flex e-con-boxed e-con\" data-id=\"064dd85\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1eeba7c elementor-widget elementor-widget-heading\" data-id=\"1eeba7c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is it?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-337e8ac elementor-widget elementor-widget-text-editor\" data-id=\"337e8ac\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>It was in 2017 that the paper &#8220;<strong>Proximal Policy Optimization Algorithms<\/strong>&#8221; was published by OpenAI researchers John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford and Oleg Klimov.<\/p><p>Their aim was to overcome the limitations of existing Reinforcement Learning algorithms, particularly in terms of training stability and handling complex action spaces.<\/p><p>When optimizing policies in<a href=\"https:\/\/datascientest.com\/en\/unraveling-machine-learning-vs-deep-learning-key-differences-explained\"> Reinforcement Learning<\/a>, overly aggressive updates can compromise training.<\/p><p>The <strong>PPO<\/strong> introduces a new notion into this process: proximity. This ensures that updates are not too far removed from previous policies.<\/p><p>This approach is inspired by the concept of &#8220;clipping&#8221;, which aims to limit the extent of updates to avoid abrupt changes. The result is more stable convergence and <strong>improved learning performance.<\/strong><\/p><p>\u00a0<\/p><p>\ud83d\udca1Related articles:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\"><colgroup><col width=\"656\" \/><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Image Processing&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/image-processing-fundamental-principles-and-practical-uses\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/image-processing-fundamental-principles-and-practical-uses\" target=\"_blank\" rel=\"noopener\">Image Processing<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Deep Learning - All you need to know&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\" target=\"_blank\" rel=\"noopener\">Deep Learning &#8211; All you need to know<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Mushroom Recognition&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/mushroom-recognition\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/mushroom-recognition\" target=\"_blank\" rel=\"noopener\">Mushroom Recognition<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Tensor Flow - Google's ML&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/tensor-flow-all-about-googles-machine-learning-framework\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/tensor-flow-all-about-googles-machine-learning-framework\" target=\"_blank\" rel=\"noopener\">Tensor Flow &#8211; Google&#8217;s ML<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Dive into ML&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/unlock-your-future-dive-into-machine-learning-engineer-training\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/unlock-your-future-dive-into-machine-learning-engineer-training\" target=\"_blank\" rel=\"noopener\">Dive into ML<\/a><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-11ca168 e-flex e-con-boxed e-con\" data-id=\"11ca168\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-10ee1e8 elementor-widget elementor-widget-heading\" data-id=\"10ee1e8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Understanding the architecture and operation of the PPO<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d51fd47 elementor-widget elementor-widget-text-editor\" data-id=\"d51fd47\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The algorithm is distinguished by its architecture, which combines key elements to enable stable and efficient learning in dynamic environments.<\/p><p>It follows an iterative approach: the agent interacts with the environment, collects training data, updates its policies according to the proximity principle, then repeats the process to improve performance over time.<\/p><p>This constant iteration is essential to enable the agent to adapt to complex and changing environments.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d0b5922 elementor-widget elementor-widget-image\" data-id=\"d0b5922\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/12\/PPO-2.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80fcd90 elementor-widget elementor-widget-text-editor\" data-id=\"80fcd90\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>One of the key components is the value function, often implemented <strong>as a state value function (V) or an advantage value function (A)<\/strong> to evaluate the quality of the actions performed by the agent.<\/p><p>The advantage represents the difference between the actual reward obtained by the agent and the predicted value. This evaluation quantifies the relevance of current policies and guides future updates.<\/p><p>Agent policies are generally stochastic: they generate a probability distribution of possible actions. In this way, the agent can introduce exploration into its learning process to better discover optimal strategies.<\/p><p>\u00a0<\/p><p>\ud83d\udca1Related articles:<\/p><table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" data-sheets-root=\"1\"><colgroup><col width=\"656\" \/><\/colgroup><tbody><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kernel: everything you need to know about the Machine Learning method&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/kernel-everything-you-need-to-know-about-the-machine-learning-method\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/kernel-everything-you-need-to-know-about-the-machine-learning-method\" target=\"_blank\" rel=\"noopener\">Kernel: everything you need to know about the Machine Learning method<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Bagging Machine Learning: What is it about?&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/bagging-machine-learning-what-is-it-about\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/bagging-machine-learning-what-is-it-about\" target=\"_blank\" rel=\"noopener\">Bagging Machine Learning: What is it about?<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Machine Learning Engineer Bootcamp: Why is it interesting?&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-bootcamp-why-is-it-interesting\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-bootcamp-why-is-it-interesting\" target=\"_blank\" rel=\"noopener\">Machine Learning Engineer Bootcamp: Why is it interesting?<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Streamlit, the tool for presenting your Machine Learning work&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/streamlit-the-tool-for-presenting-your-machine-learning-work\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/streamlit-the-tool-for-presenting-your-machine-learning-work\" target=\"_blank\" rel=\"noopener\">Streamlit, the tool for presenting your Machine Learning work<\/a><\/td><\/tr><tr><td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;CatBoost: An essential Machine Learning tool&quot;}\" data-sheets-hyperlink=\"https:\/\/datascientest.com\/en\/catboost-an-essential-machine-learning-tool\"><a class=\"in-cell-link\" href=\"https:\/\/datascientest.com\/en\/catboost-an-essential-machine-learning-tool\" target=\"_blank\" rel=\"noopener\">CatBoost: An essential Machine Learning tool<\/a><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-99a0728 e-flex e-con-boxed e-con\" data-id=\"99a0728\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2c6d950 elementor-widget elementor-widget-heading\" data-id=\"2c6d950\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How does the optimization process work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f051266 elementor-widget elementor-widget-text-editor\" data-id=\"f051266\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>It all starts with th<em>e agent&#8217;s interaction with the environment.<\/em> It performs actions according to its current policy, observes the resulting state of the environment, and receives a reward or penalty.<\/p><p>These interactions <strong>generate data trajectories,<\/strong> which are then used to update the agent&#8217;s policy.<\/p><p>Once the data trajectories have been collected, the agent calculates the benefits by measuring the relative performance of each action against the predicted value.<\/p><p>This step enables it to determine which actions have contributed positively or negatively to the reward obtained by the agent. Depending on the result, the policy is updated using algorithms such as stochastic gradient descent.<\/p><p>The<strong> aim is to maximize the probability<\/strong> of the most advantageous actions. However, the proximity constraint limits policy changes to a certain threshold.<\/p><p>The iterative process is repeated several times, allowing the agent to gradually adjust to its environment and learn more efficient policies over time.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68924e6 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"68924e6\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Mastering how the PPO works<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-504d3ea e-flex e-con-boxed e-con\" data-id=\"504d3ea\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c46f8e9 elementor-widget elementor-widget-heading\" data-id=\"c46f8e9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the advantages of PPO?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c4e3f58 elementor-widget elementor-widget-text-editor\" data-id=\"c4e3f58\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The <strong>use of this algorithm brings several major advantages.<\/strong> Firstly, as mentioned above, the proximity constraint contributes significantly to the stability of the training.<\/p><p>It avoids abrupt changes that could compromise the convergence of the algorithm. What&#8217;s more, <strong>PPO<\/strong> excels at managing extended spaces, enabling agents to handle complex environments with numerous and diverse actions.<\/p><p>Its flexibility also makes it adaptable to a wide variety of <strong>Reinforcement Learning<\/strong> tasks and application sectors. To better illustrate its advantages, let&#8217;s compare it with other algorithms.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-605471a e-flex e-con-boxed e-con\" data-id=\"605471a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a88e22a elementor-widget elementor-widget-heading\" data-id=\"a88e22a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">PPO compared with other RL algorithms<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a370b6f elementor-widget elementor-widget-text-editor\" data-id=\"a370b6f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The<strong> Reinforcement Learning<\/strong> landscape is rich in algorithms. A comparison helps us to better understand PPO&#8217;s distinctive advantages and its position within this sphere.<\/p><p>One of the best known is <strong>DDPG (Deep Deterministic Policy Gradients)<\/strong>, which distinguishes itself by tackling problems involving continuous action spaces, where possible actions form an infinite set.<\/p><p>Unlike PPO, which excels at managing stochastic action spaces, DDPG uses deterministic policy. This means that it assigns a specific action to a given state, rather than a probability distribution.<\/p><p><strong>TRPO (Trust Region Policy Optimization)<\/strong> shares with PPO the idea of maintaining stability when optimizing policies. However, it uses a trust region approach to limit policy changes.<\/p><p>This differs from PPO, which opts for a proximity constraint. This simplicity often makes it easier to implement and less sensitive to hyperparameters.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-85ce591 elementor-widget elementor-widget-image\" data-id=\"85ce591\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/12\/PPO-1.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-31e2642 elementor-widget elementor-widget-text-editor\" data-id=\"31e2642\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Another algorithm:<\/strong> SAC or Soft Actor-Critic. It focuses on learning efficiency in intensive exploration environments. Its entropy maximization encourages exploration, and distinguishes it from PPO. However, SAC may be more sensitive to the choice of hyperparameters and require <strong>fine-tuning for optimal performance.<\/strong><\/p><p>Overall, PPO shines for its conceptual simplicity and ease of implementation, while maintaining solid performance. Its iterative approach with proximity constraint proves particularly beneficial in practical applications, as we shall now see.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d71ebba e-flex e-con-boxed e-con\" data-id=\"d71ebba\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-45d8664 elementor-widget elementor-widget-heading\" data-id=\"45d8664\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What are the main applications?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a6c931b elementor-widget elementor-widget-text-editor\" data-id=\"a6c931b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The PPO has demonstrated outstanding performance in complex video games. A notable example is the AlphaGO AI. The algorithm has been used to train agents capable of outperforming human Go champions.<\/p><p>It has also been successfully applied to enable robots to learn complex tasks such as manipulating various objects in dynamic environments. It is therefore one of the algorithms at the heart of the upcoming humanoid robot revolution, which includes the Tesla Optimus.<\/p><p>In the financial sector, PPO is used to optimize automated trading strategies. Its stability and adaptability to changing market conditions make it an attractive choice for these sensitive applications.<\/p><p>And in the healthcare sector, it is used to design personalized treatment policies. It helps, for example, to dynamically adjust treatment protocols according to individual patient response.<\/p><p>This wide range of applications makes it a key algorithm in the new wave of artificial intelligences that are invading every field. And this is just the beginning: there are many more developments on the horizon&#8230;<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2fd328 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a2fd328\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn how to use the PPO<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-236a08e e-flex e-con-boxed e-con\" data-id=\"236a08e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-92136cb elementor-widget elementor-widget-heading\" data-id=\"92136cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">PPO2 and future algorithm developments<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f5411f elementor-widget elementor-widget-text-editor\" data-id=\"4f5411f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>A second &#8220;<a href=\"https:\/\/datascientest.com\/en\/harnessing-the-power-of-gpus-in-data-science-what-you-need-to-know\">GPU<\/a>-enabled&#8221; implementation called PPO2 has also been released by OpenAI. It runs three times faster than the Atari baseline.<\/p><p>In addition, the American firm also launched an implementation of the ACER (Actor Critic with Experience Replay) algorithm, which uses a replay buffer and a Q-Function trained with Retrace.<\/p><p>Several variants have emerged to solve more specific problems. Some introduce more sophisticated exploration mechanisms, while others focus on more advanced optimization strategies.<\/p><p><strong>Research<\/strong> has explored the dynamic adaptation of hyperparameters for automatic adjustment to changing environmental or task characteristics.<\/p><p>The algorithm is increasingly integrated with imitation learning approaches, where the agent learns from human demonstrations. This integration facilitates the rapid acquisition of effective policies.<\/p><p>Researchers are also investigating the potential of learning transfer with <strong>PPO<\/strong>, to enable agents to apply knowledge acquired in one domain to related tasks and accelerate learning in new contexts.<\/p><p>In the future, we can expect more efficient exploration mechanisms, better management of high-dimensional action spaces for application to even more complex tasks, and enhanced interpretability of learned policies to make agents&#8217; <strong>decisions more comprehensible.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1fe7a58 elementor-widget elementor-widget-image\" data-id=\"1fe7a58\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/12\/PPO-3.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ea2a72a e-flex e-con-boxed e-con\" data-id=\"ea2a72a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6beb057 elementor-widget elementor-widget-heading\" data-id=\"6beb057\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion: Proximal Policy Optimization, a balance between stability and RL efficiency<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-93cdd37 elementor-widget elementor-widget-text-editor\" data-id=\"93cdd37\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Thanks to the notion of proximity, which prevents over-aggressive policy updates, <strong>PPO avoids undesirable oscillations in reinforcement learning.<\/strong> This perfect balance between stability and efficiency enables it to adapt to a wider variety of tasks.<\/p><p>Over the years, the <strong>algorithm<\/strong> has gained in popularity due to its ability to handle complex environments such as video games, robotics, finance and healthcare. It has become a benchmark for many applications.<\/p><p>To become an expert in<a href=\"https:\/\/datascientest.com\/en\/catboost-an-essential-machine-learning-tool\"> Machine Learning,<\/a> Reinforcement Learning and Artificial Intelligence, you can turn to DataScientest. Our distance learning courses enable you to acquire real mastery in record time!<\/p><p>The Data Scientist course covers Python programming, DataViz, machine learning and deep learning techniques, data engineering and <a href=\"https:\/\/datascientest.com\/en\/mlops-devops-applied-to-machine-learning-projects\">MLOPS<\/a>.<\/p><p>The module dedicated to complex models covers Reinforcement Learning, recommendation systems and graph theory. By the end of the course, you will have acquired all the skills required to become a Data Scientist.<\/p><p>You will receive a &#8220;Project Manager in Artificial Intelligence&#8221; certification from the Coll\u00e8ge de Paris, a certificate from Mines ParisTech PSL Executive Education and an AWS Cloud Practitioner certification.<\/p><p>To take things a step further, we also offer a Machine Learning Engineer course. This combines the Data Scientist curriculum with modules dedicated to the development and deployment of artificial intelligence systems.<\/p><p>At a time when generative AI tools such as ChatGPT and <a href=\"https:\/\/datascientest.com\/en\/dall-e-a-source-of-inspiration-for-the-creative-professions\"><strong>DALL-E<\/strong> are booming,<\/a> our Prompt Engineering &amp; Generative AI training course will enable you to learn how to master these new tools by becoming a master in the art of formulating prompts.<\/p><p>Our various courses can be completed in an intensive BootCamp, either full-time or part-time. As far as financing is concerned, our state-recognized organization is eligible for funding options. Discover DataScientest!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-93f080d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"93f080d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9879265 elementor-widget elementor-widget-html\" data-id=\"9879265\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is Proximal Policy Optimization?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"It was in 2017 that the article \"Proximal Policy Optimization Algorithms\" was published by OpenAI researchers John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford and Oleg Klimov. Their aim was to overcome the limitations of existing Reinforcement Learning algorithms, particularly in terms of training stability and handling complex action spaces. When optimizing policies in Reinforcement Learning, overly aggressive updates can compromise training. The PPO introduces a new notion into this process: proximity. This ensures that updates are not too far removed from previous policies.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How does the optimization process work?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"It all starts with the agent's interaction with the environment. It performs actions according to its current policy, observes the resulting state of the environment, and receives a reward or penalty. These interactions generate data trajectories, which are then used to update the agent's policy. Once the data trajectories have been collected, the agent calculates the benefits by measuring the relative performance of each action against the predicted value. This step enables it to determine which actions have contributed positively or negatively to the reward obtained by the agent. Depending on the result, the policy is updated using algorithms such as stochastic gradient descent. The aim is to maximize the probability of the most advantageous actions. However, the proximity constraint limits policy changes to a certain threshold. The iterative process is repeated several times, allowing the agent to gradually adjust to its environment and learn more efficient policies over time.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What are the benefits of PPO?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Using this algorithm brings several major advantages. Firstly, as mentioned above, the proximity constraint contributes significantly to the stability of the drive. It avoids abrupt changes that could compromise the convergence of the algorithm. What's more, PPO excels at managing extended spaces, enabling agents to handle complex environments with numerous and diverse actions. Its flexibility also makes it adaptable to a wide variety of Reinforcement Learning tasks and application sectors. To better illustrate its advantages, let's now compare it with other algorithms.\"\n    }\n  }]\n}\n<\/script>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Proximal Policy Optimization is a Reinforcement Learning algorithm created by OpenAI, ideal for complex environments such as video games or robotics. Find out all you need to know about its history, how it works and how to use it! In the field of Machine Learning, Reinforcement Learning has been enjoying a remarkable boom over the [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":183242,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Proximal Policy Optimization: all about the algorithm created by OpenAI<\/title>\n<meta name=\"description\" content=\"Proximal Policy Optimization (PPO) is a reinforcement learning algorithm developed by OpenAI that is designed for training ..\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/proximal-policy-optimization-all-about-the-algorithm-created-by-openai\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Proximal Policy Optimization: all about the algorithm created by OpenAI\" \/>\n<meta property=\"og:description\" content=\"Proximal Policy Optimization is a Reinforcement Learning algorithm created by OpenAI, ideal for complex environments such as video games or robotics. 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