Reinforcement learning - GeeksforGeeks

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This tutorial will guide you through the fundamentals of reinforcement learning, its key components, and its applications. This guide is perfect for students, professionals, and AI enthusiasts who want to enhance their knowledge of machine learning and artificial intelligence.<\/p><h3>Introduction to Reinforcement Learning<\/h3><p>Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative reward. Unlike supervised learning, where the model is trained on a dataset with labeled examples, reinforcement learning involves learning through interaction with the environment.<\/p><h3>Key Components of Reinforcement Learning<\/h3><ol><li><strong>Agent<\/strong>: The learner or decision-maker that interacts with the environment.<\/li><li><strong>Environment<\/strong>: The external system with which the agent interacts. It provides feedback in the form of rewards or punishments based on the agent's actions.<\/li><li><strong>State<\/strong>: A representation of the current situation or configuration of the environment.<\/li><li><strong>Action<\/strong>: The set of all possible moves the agent can make in the environment.<\/li><li><strong>Reward<\/strong>: The feedback received from the environment as a result of the agent's actions. The goal of the agent is to maximize the cumulative reward over time.<\/li><li><strong>Policy<\/strong>: The strategy that the agent employs to determine the next action based on the current state.<\/li><li><strong>Value Function<\/strong>: A function that estimates the expected cumulative reward of being in a particular state or taking a particular action.<\/li><\/ol><h3>How Reinforcement Learning Works<\/h3><p>Reinforcement learning is based on the idea of agents taking actions to achieve the maximum cumulative reward. The agent observes the current state of the environment, takes an action, and receives feedback in the form of a reward. This process continues, and the agent learns to improve its actions over time by maximizing the cumulative reward.<\/p><ol><li><strong>Initialization<\/strong>: The agent starts with an initial policy and value function.<\/li><li><strong>Interaction<\/strong>: The agent interacts with the environment by taking actions.<\/li><li><strong>Feedback<\/strong>: The environment provides feedback in the form of rewards.<\/li><li><strong>Update<\/strong>: The agent updates its policy and value function based on the received rewards.<\/li><li><strong>Iteration<\/strong>: This process repeats, with the agent continually refining its strategy to maximize the cumulative reward.<\/li><\/ol><h3>Types of Reinforcement Learning<\/h3><ol><li><strong>Model-Free Reinforcement Learning<\/strong>: The agent learns directly from interactions with the environment without building a model of the environment. Examples include Q-learning and SARSA.<\/li><li><strong>Model-Based Reinforcement Learning<\/strong>: The agent builds a model of the environment and uses it to plan and make decisions. This approach can be more efficient but requires accurate modeling of the environment.<\/li><\/ol><h3>Applications of Reinforcement Learning<\/h3><p>Reinforcement learning has a wide range of applications across various fields, including:<\/p><ol><li><strong>Robotics<\/strong>: Teaching robots to perform tasks autonomously by learning from interactions with their environment.<\/li><li><strong>Game Playing<\/strong>: Developing AI agents that can play and win games, such as AlphaGo, which defeated human champions in the game of Go.<\/li><li><strong>Autonomous Vehicles<\/strong>: Enabling self-driving cars to navigate complex environments by learning from real-world interactions.<\/li><li><strong>Finance<\/strong>: Using RL for algorithmic trading, portfolio management, and optimizing investment strategies.<\/li><li><strong>Healthcare<\/strong>: Personalizing treatment plans and optimizing clinical decision-making by learning from patient data.<\/li><\/ol><h3>Conclusion<\/h3><p>Reinforcement learning is a powerful machine learning technique that enables agents to learn from interactions with their environment to make optimal decisions. By understanding the key components and principles of reinforcement learning, you can appreciate its potential and explore its various applications.<\/p><p>Whether you are a student, a professional, or an AI enthusiast, learning about reinforcement learning can significantly enhance your understanding of artificial intelligence and machine learning. Reinforcement learning's ability to solve complex decision-making problems makes it a valuable tool in various domains.<\/p><p>For a detailed step-by-step guide, check out the full article: <a href=\\\"https:\/\/www.geeksforgeeks.org\/what-is-reinforcement-learning\/\\\">What is Reinforcement Learning?<\/a>.<\/p>\",\"source\":\"https:\/\/videos.geeksforgeeks.org\/hls\/0c3c7fb2a530e25573baf590f511e8dagfg-Reinforcementlearning20240808124643.m3u8\",\"category\":[{\"term_id__id\":37,\"term_id__term_name\":\"ML\",\"term_id__term_type\":2,\"term_id__slug\":\"ml\"},{\"term_id__id\":116,\"term_id__term_name\":\"Machine Learning\",\"term_id__term_type\":1,\"term_id__slug\":\"machine-learning\"},{\"term_id__id\":338,\"term_id__term_name\":\"machine-learning\",\"term_id__term_type\":2,\"term_id__slug\":\"machine-learning-c23scf\"}],\"video_type\":\"geeksforgeeks\",\"meta\":{\"thumbnail\":\"https:\/\/media.geeksforgeeks.org\/geeksforgeeks\/ReinforcementlearninginMachineLearning\/Reinforcementlearning20240808124647-small.png\",\"largeThumbnail\":\"https:\/\/media.geeksforgeeks.org\/geeksforgeeks\/ReinforcementlearninginMachineLearning\/Reinforcementlearning20240808124647.png\",\"likes\":0,\"views\":9060,\"isFeatured\":0,\"isPremium\":0,\"isPublic\":0,\"format\":\"video\/mp4\",\"revision\":{}},\"time\":\"08\/08\/2024\",\"seo\":{\"description\":\"<h2>What is Reinforcement Learning?<\/h2><p>Are you interested in understand\",\"thumbnail\":\"https:\/\/media.geeksforgeeks.org\/geeksforgeeks\/ReinforcementlearninginMachineLearning\/Reinforcementlearning20240808124647-seo.png\"},\"subtitle\":\"https:\/\/videos.geeksforgeeks.org\/subtitles\/Reinforcementlearning20240808124643.vtt\",\"duration\":435,\"post_list\":[{\"post_id\":195593,\"post_title\":\"Reinforcement learning\"}],\"course_link\":null,\"views\":\"9060\",\"video_schema\":{\"@context\":\"https:\/\/schema.org\",\"@type\":\"VideoObject\",\"name\":\"Reinforcement learning in Machine Learning\",\"description\":\"What is Reinforcement Learning?Are you interested in understanding the concept of reinforcement learning and how it works? This tutorial will guide you through the fundamentals of reinforcement learning, its key components, and its applications. This guide is perfect for students, professionals, and AI enthusiasts who want to enhance their knowledge of machine learning and artificial intelligence.Introduction to Reinforcement LearningReinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative reward. Unlike supervised learning, where the model is trained on a dataset with labeled examples, reinforcement learning involves learning through interaction with the environment.Key Components of Reinforcement LearningAgent The learner or decision-maker that interacts with the environment.Environment The external system with which the agent interacts. It provides feedback in the form of rewards or punishments based on the agents actions.State A representation of the current situation or configuration of the environment.Action The set of all possible moves the agent can make in the environment.Reward The feedback received from the environment as a result of the agents actions. The goal of the agent is to maximize the cumulative reward over time.Policy The strategy that the agent employs to determine the next action based on the current state.Value Function A function that estimates the expected cumulative reward of being in a particular state or taking a particular action.How Reinforcement Learning WorksReinforcement learning is based on the idea of agents taking actions to achieve the maximum cumulative reward. The agent observes the current state of the environment, takes an action, and receives feedback in the form of a reward. This process continues, and the agent learns to improve its actions over time by maximizing the cumulative reward.Initialization The agent starts with an initial policy and value function.Interaction The agent interacts with the environment by taking actions.Feedback The environment provides feedback in the form of rewards.Update The agent updates its policy and value function based on the received rewards.Iteration This process repeats, with the agent continually refining its strategy to maximize the cumulative reward.Types of Reinforcement LearningModel-Free Reinforcement Learning The agent learns directly from interactions with the environment without building a model of the environment. Examples include Q-learning and SARSA.Model-Based Reinforcement Learning The agent builds a model of the environment and uses it to plan and make decisions. This approach can be more efficient but requires accurate modeling of the environment.Applications of Reinforcement LearningReinforcement learning has a wide range of applications across various fields, includingRobotics Teaching robots to perform tasks autonomously by learning from interactions with their environment.Game Playing Developing AI agents that can play and win games, such as AlphaGo, which defeated human champions in the game of Go.Autonomous Vehicles Enabling self-driving cars to navigate complex environments by learning from real-world interactions.Finance Using RL for algorithmic trading, portfolio management, and optimizing investment strategies.Healthcare Personalizing treatment plans and optimizing clinical decision-making by learning from patient data.ConclusionReinforcement learning is a powerful machine learning technique that enables agents to learn from interactions with their environment to make optimal decisions. By understanding the key components and principles of reinforcement learning, you can appreciate its potential and explore its various applications.Whether you are a student, a professional, or an AI enthusiast, learning about reinforcement learning can significantly enhance your understanding of artificial intelligence and machine learning. Reinforcement learnings ability to solve complex decision-making problems makes it a valuable tool in various domains.For a detailed step-by-step guide, check out the full article What is Reinforcement Learning?.\",\"thumbnailUrl\":[\"https:\/\/media.geeksforgeeks.org\/geeksforgeeks\/ReinforcementlearninginMachineLearning\/Reinforcementlearning20240808124647-seo.png\",\"https:\/\/media.geeksforgeeks.org\/geeksforgeeks\/ReinforcementlearninginMachineLearning\/Reinforcementlearning20240808124647-small.png\",\"https:\/\/media.geeksforgeeks.org\/geeksforgeeks\/ReinforcementlearninginMachineLearning\/Reinforcementlearning20240808124647.png\"],\"uploadDate\":\"2024-08-08T12:49:01Z\",\"duration\":\"PT0H7M15S\",\"contentUrl\":\"https:\/\/www.geeksforgeeks.org\/videos\/reinforcement-learning-in-machine-learning\/\"}}"); </script>  <script defer src="https://assets.geeksforgeeks.org/codetabs/v3/codetabs.js" environment="production" domain="gfg" no-scroll></script> <title>Reinforcement learning - GeeksforGeeks</title> <link rel="profile" href="http://gmpg.org/xfn/11" /> <link rel="pingback" href="" />   <script type="application/ld+json">{"@context":"https://schema.org","@type":"VideoObject","name":"Reinforcement learning in Machine Learning","description":"What is Reinforcement Learning?Are you interested in understanding the concept of reinforcement learning and how it works? This tutorial will guide you through the fundamentals of reinforcement learning, its key components, and its applications. This guide is perfect for students, professionals, and AI enthusiasts who want to enhance their knowledge of machine learning and artificial intelligence.Introduction to Reinforcement LearningReinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative reward. Unlike supervised learning, where the model is trained on a dataset with labeled examples, reinforcement learning involves learning through interaction with the environment.Key Components of Reinforcement LearningAgent The learner or decision-maker that interacts with the environment.Environment The external system with which the agent interacts. It provides feedback in the form of rewards or punishments based on the agents actions.State A representation of the current situation or configuration of the environment.Action The set of all possible moves the agent can make in the environment.Reward The feedback received from the environment as a result of the agents actions. The goal of the agent is to maximize the cumulative reward over time.Policy The strategy that the agent employs to determine the next action based on the current state.Value Function A function that estimates the expected cumulative reward of being in a particular state or taking a particular action.How Reinforcement Learning WorksReinforcement learning is based on the idea of agents taking actions to achieve the maximum cumulative reward. The agent observes the current state of the environment, takes an action, and receives feedback in the form of a reward. This process continues, and the agent learns to improve its actions over time by maximizing the cumulative reward.Initialization The agent starts with an initial policy and value function.Interaction The agent interacts with the environment by taking actions.Feedback The environment provides feedback in the form of rewards.Update The agent updates its policy and value function based on the received rewards.Iteration This process repeats, with the agent continually refining its strategy to maximize the cumulative reward.Types of Reinforcement LearningModel-Free Reinforcement Learning The agent learns directly from interactions with the environment without building a model of the environment. Examples include Q-learning and SARSA.Model-Based Reinforcement Learning The agent builds a model of the environment and uses it to plan and make decisions. This approach can be more efficient but requires accurate modeling of the environment.Applications of Reinforcement LearningReinforcement learning has a wide range of applications across various fields, includingRobotics Teaching robots to perform tasks autonomously by learning from interactions with their environment.Game Playing Developing AI agents that can play and win games, such as AlphaGo, which defeated human champions in the game of Go.Autonomous Vehicles Enabling self-driving cars to navigate complex environments by learning from real-world interactions.Finance Using RL for algorithmic trading, portfolio management, and optimizing investment strategies.Healthcare Personalizing treatment plans and optimizing clinical decision-making by learning from patient data.ConclusionReinforcement learning is a powerful machine learning technique that enables agents to learn from interactions with their environment to make optimal decisions. By understanding the key components and principles of reinforcement learning, you can appreciate its potential and explore its various applications.Whether you are a student, a professional, or an AI enthusiast, learning about reinforcement learning can significantly enhance your understanding of artificial intelligence and machine learning. Reinforcement learnings ability to solve complex decision-making problems makes it a valuable tool in various domains.For a detailed step-by-step guide, check out the full article What is Reinforcement Learning?.","thumbnailUrl":["https://media.geeksforgeeks.org/geeksforgeeks/ReinforcementlearninginMachineLearning/Reinforcementlearning20240808124647-seo.png","https://media.geeksforgeeks.org/geeksforgeeks/ReinforcementlearninginMachineLearning/Reinforcementlearning20240808124647-small.png","https://media.geeksforgeeks.org/geeksforgeeks/ReinforcementlearninginMachineLearning/Reinforcementlearning20240808124647.png"],"uploadDate":"2024-08-08T12:49:01Z","duration":"PT0H7M15S","contentUrl":"https://www.geeksforgeeks.org/videos/reinforcement-learning-in-machine-learning/"}</script>   <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "Article", "mainEntityOfPage": { "@type": "WebPage", "id": "https://www.geeksforgeeks.org/what-is-reinforcement-learning/" }, "headline": "Reinforcement learning", "datePublished": "2018-04-25 08:56:13", "dateModified": "2024-09-04 12:40:34", "image": { "@type": "ImageObject", "url": "https://media.geeksforgeeks.org/wp-content/uploads/Untitled-95.png", "width": "996", "height": "689" }, "author": { "@type": "Organization", "name": "GeeksforGeeks", "url": "https://www.geeksforgeeks.org/", "logo": { "@type": "ImageObject", "url": "https://media.geeksforgeeks.org/wp-content/cdn-uploads/logo-new-2.svg", "width": "301", "height": "40" } }, "publisher": { "@type": "Organization", "name": "GeeksforGeeks", "url": "https://www.geeksforgeeks.org/", "logo": { "@type": "ImageObject", "url": "https://media.geeksforgeeks.org/wp-content/cdn-uploads/logo-new-2.svg", "width": "301", "height": "40" } }, "description": "Reinforcement Learning: An OverviewReinforcement Learning (RL) is a branch of machine learning focused on making decisions to maximize cumulative rewards in a given situation. 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Tutorial</a></li></ul></li><li class="mega-dropdown__list-item" data-parent="false" aria-expanded="true" data-expandable="true"><span>Sorting Algorithms</span><i class="gfg-icon gfg-icon_arrow-right"></i><ul class="mega-dropdown Screen_3"><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/selection-sort/?ref=outind" target="_self">Selection Sort</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/bubble-sort/?ref=outind" target="_self">Bubble Sort</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/insertion-sort/?ref=outind" target="_self">Insertion Sort</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a 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alt="News"></img> <span class='kebab_menu_news_text'>Follow</span> </a> </div> </div> </div> <div class="text"> <h2><span>Reinforcement Learning: An Overview</span></h2><p dir="ltr" style="text-align: justify;"><span>Reinforcement Learning (RL) is a branch of machine learning focused on making decisions to maximize cumulative rewards in a given situation. Unlike supervised learning, which relies on a training dataset with predefined answers, RL involves learning through experience. In RL, an agent learns to achieve a goal in an uncertain, potentially complex environment by performing actions and receiving feedback through rewards or penalties.</span></p> <h3><span>Key Concepts of Reinforcement Learning</span></h3><ul><li value="1"><b><strong>Agent:</strong></b><span> The learner or decision-maker.</span></li><li value="2"><b><strong>Environment: </strong></b><span>Everything the agent interacts with.</span></li><li value="3"><b><strong>State:</strong></b><span> A specific situation in which the agent finds itself.</span></li><li value="4"><b><strong>Action: </strong></b><span>All possible moves the agent can make.</span></li><li value="5"><b><strong>Reward: </strong></b><span>Feedback from the environment based on the action taken.</span></li></ul><h3><span>How Reinforcement Learning Works</span></h3><p dir="ltr" style="text-align: justify;"><span>RL operates on the principle of learning optimal behavior through trial and error. The agent takes actions within the environment, receives rewards or penalties, and adjusts its behavior to maximize the cumulative reward. This learning process is characterized by the following elements:</span></p><div id="GFG_AD_gfg_mobile_336x280" style="margin: 5px 0;"></div> <ul><li value="1"><b><strong>Policy:</strong></b><span> A strategy used by the agent to determine the next action based on the current state.</span></li><li value="2"><b><strong>Reward Function:</strong></b><span> A function that provides a scalar feedback signal based on the state and action.</span></li><li value="3"><b><strong>Value Function:</strong></b><span> A function that estimates the expected cumulative reward from a given state.</span></li><li value="4"><b><strong>Model of the Environment:</strong></b><span> A representation of the environment that helps in planning by predicting future states and rewards.</span></li></ul><h3><span>Example: Navigating a Maze</span></h3><p dir="ltr" style="text-align: justify;"><span>The problem is as follows: We have an agent and a reward, with many hurdles in between. The agent is supposed to find the best possible path to reach the reward. The following problem explains the problem more easily.  </span></p> <p dir="ltr"><img src="https://media.geeksforgeeks.org/wp-content/uploads/Untitled-95.png" width="inherit" height="inherit"></p> <p dir="ltr" style="text-align: justify;"><br><span>The above image shows the robot, diamond, and fire. The goal of the robot is to get the reward that is the diamond and avoid the hurdles that are fired. The robot learns by trying all the possible paths and then choosing the path which gives him the reward with the least hurdles. Each right step will give the robot a reward and each wrong step will subtract the reward of the robot. The total reward will be calculated when it reaches the final reward that is the diamond. </span></p> <h4><br><b><strong>Main points in Reinforcement learning –</strong></b><span> </span></h4><ul><li value="1"><span>Input: The input should be an initial state from which the model will start</span></li><li value="2"><span>Output: There are many possible outputs as there are a variety of solutions to a particular problem</span></li><li value="3"><span>Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.</span></li><li value="4"><span>The model keeps continues to learn.</span></li><li value="5"><span>The best solution is decided based on the maximum reward.</span></li></ul><div> </div><h3><span>Difference between Reinforcement learning and Supervised learning: </span></h3><table><thead><tr><th><span>Reinforcement learning</span></th><th><span>Supervised learning</span></th></tr></thead><tbody><tr><td><span>Reinforcement learning is all about making decisions sequentially. In simple words, we can say that the output depends on the state of the current input and the next input depends on the output of the previous input</span></td><td><span>In Supervised learning, the decision is made on the initial input or the input given at the start</span></td></tr><tr><td><span>In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions</span></td><td><span>In supervised learning the decisions are independent of each other so labels are given to each decision.</span></td></tr><tr><td><span>Example: Chess game,text summarization</span></td><td><span>Example: Object recognition,spam detetction</span></td></tr></tbody></table><h3><span>Types of Reinforcement: </span></h3><ol><li value="1"><b><strong>Positive: </strong></b><span>Positive Reinforcement is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior. In other words, it has a positive effect on behavior. </span><br><span>Advantages of reinforcement learning are: </span><br><ul><li value="1"><span>Maximizes Performance</span></li><li value="2"><span>Sustain Change for a long period of time</span></li><li value="3"><span>Too much Reinforcement can lead to an overload of states which can diminish the results</span></li></ul></li><li value="2"><b><strong>Negative: </strong></b><span>Negative Reinforcement is defined as strengthening of behavior because a negative condition is stopped or avoided. </span><br><span>Advantages of reinforcement learning: </span><br><ul><li value="1"><span>Increases Behavior</span></li><li value="2"><span>Provide defiance to a minimum standard of performance</span></li><li value="3"><span>It Only provides enough to meet up the minimum behavior</span></li></ul></li></ol><h3><span>Elements of Reinforcement Learning</span></h3><p dir="ltr"><b><strong>i) Policy:</strong></b><span> Defines the agent鈥檚 behavior at a given time.</span></p> <p dir="ltr"><b><strong>ii) Reward Function: </strong></b><span>Defines the goal of the RL problem by providing feedback.</span></p><div id="GFG_AD_Desktop_InContent_ATF_336x280" style="text-align:center; max-height: 280px;"></div><div id="GFG_AD_gfg_mobile_336x280_1" style="margin: 5px 0;"></div> <p dir="ltr"><b><strong>iii) Value Function:</strong></b><span> Estimates long-term rewards from a state.</span></p><div id="_GFG_ABP_Incontent_728x90" style="text-align:center;"></div> <p dir="ltr"><b><strong>iv) Model of the Environment:</strong></b><span> Helps in predicting future states and rewards for planning.</span></p> <h2><span>Example: CartPole Environment in OpenAI Gym</span></h2><p dir="ltr"><span>The CartPole environment is a classic reinforcement learning problem where the goal is to balance a pole on a cart by applying forces to the left or right.</span></p> <gfg-tabs data-run-ide="true" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3"> <code class="language-python3"><div class=highlight><pre><span></span><span class=kn>import</span> <span class=nn>gym</span> <span class=kn>import</span> <span class=nn>numpy</span> <span class=k>as</span> <span class=nn>np</span> <span class=kn>import</span> <span class=nn>warnings</span> <span class=c1># Suppress specific deprecation warnings</span> <span class=n>warnings</span><span class=o>.</span><span class=n>filterwarnings</span><span class=p>(</span><span class=s2>"ignore"</span><span class=p>,</span> <span class=n>category</span><span class=o>=</span><span class=ne>DeprecationWarning</span><span class=p>)</span> <span class=c1># Load the environment with render mode specified</span> <span class=n>env</span> <span class=o>=</span> <span class=n>gym</span><span class=o>.</span><span class=n>make</span><span class=p>(</span><span class=s1>'CartPole-v1'</span><span class=p>,</span> <span class=n>render_mode</span><span class=o>=</span><span class=s2>"human"</span><span class=p>)</span> <span class=c1># Initialize the environment to get the initial state</span> <span class=n>state</span> <span class=o>=</span> <span class=n>env</span><span class=o>.</span><span class=n>reset</span><span class=p>()</span> <span class=c1># Print the state space and action space</span> <span class=nb>print</span><span class=p>(</span><span class=s2>"State space:"</span><span class=p>,</span> <span class=n>env</span><span class=o>.</span><span class=n>observation_space</span><span class=p>)</span> <span class=nb>print</span><span class=p>(</span><span class=s2>"Action space:"</span><span class=p>,</span> <span class=n>env</span><span class=o>.</span><span class=n>action_space</span><span class=p>)</span> <span class=c1># Run a few steps in the environment with random actions</span> <span class=k>for</span> <span class=n>_</span> <span class=ow>in</span> <span class=nb>range</span><span class=p>(</span><span class=mi>10</span><span class=p>):</span> <span class=n>env</span><span class=o>.</span><span class=n>render</span><span class=p>()</span> <span class=c1># Render the environment for visualization</span> <span class=n>action</span> <span class=o>=</span> <span class=n>env</span><span class=o>.</span><span class=n>action_space</span><span class=o>.</span><span class=n>sample</span><span class=p>()</span> <span class=c1># Take a random action</span> <span class=c1># Take a step in the environment</span> <span class=n>step_result</span> <span class=o>=</span> <span class=n>env</span><span class=o>.</span><span class=n>step</span><span class=p>(</span><span class=n>action</span><span class=p>)</span> <span class=c1># Check the number of values returned and unpack accordingly</span> <span class=k>if</span> <span class=nb>len</span><span class=p>(</span><span class=n>step_result</span><span class=p>)</span> <span class=o>==</span> <span class=mi>4</span><span class=p>:</span> <span class=n>next_state</span><span class=p>,</span> <span class=n>reward</span><span class=p>,</span> <span class=n>done</span><span class=p>,</span> <span class=n>info</span> <span class=o>=</span> <span class=n>step_result</span> <span class=n>terminated</span> <span class=o>=</span> <span class=kc>False</span> <span class=k>else</span><span class=p>:</span> <span class=n>next_state</span><span class=p>,</span> <span class=n>reward</span><span class=p>,</span> <span class=n>done</span><span class=p>,</span> <span class=n>truncated</span><span class=p>,</span> <span class=n>info</span> <span class=o>=</span> <span class=n>step_result</span> <span class=n>terminated</span> <span class=o>=</span> <span class=n>done</span> <span class=ow>or</span> <span class=n>truncated</span> <span class=nb>print</span><span class=p>(</span><span class=sa>f</span><span class=s2>"Action: </span><span class=si>{</span><span class=n>action</span><span class=si>}</span><span class=s2>, Reward: </span><span class=si>{</span><span class=n>reward</span><span class=si>}</span><span class=s2>, Next State: </span><span class=si>{</span><span class=n>next_state</span><span class=si>}</span><span class=s2>, Done: </span><span class=si>{</span><span class=n>done</span><span class=si>}</span><span class=s2>, Info: </span><span class=si>{</span><span class=n>info</span><span class=si>}</span><span class=s2>"</span><span class=p>)</span> <span class=k>if</span> <span class=n>terminated</span><span class=p>:</span> <span class=n>state</span> <span class=o>=</span> <span class=n>env</span><span class=o>.</span><span class=n>reset</span><span class=p>()</span> <span class=c1># Reset the environment if the episode is finished</span> <span class=n>env</span><span class=o>.</span><span class=n>close</span><span class=p>()</span> <span class=c1># Close the environment when done</span> </pre></div></code> </gfg-panel></gfg-tabs><h3><span>Output:</span></h3><pre><span>State space: Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32)</span><br><span>Action space: Discrete(2)</span><br><span>Action: 0, Reward: 1.0, Next State: [ 0.00661033 -0.21114323 -0.00940697 0.30795237], Done: False, Info: {}</span><br><span>Action: 0, Reward: 1.0, Next State: [ 0.00238747 -0.40612987 -0.00324792 0.5976538 ], Done: False, Info: {}</span><br><span>Action: 0, Reward: 1.0, Next State: [-0.00573513 -0.60120624 0.00870516 0.8893119 ], Done: False, Info: {}</span><br><span>Action: 0, Reward: 1.0, Next State: [-0.01775926 -0.79644525 0.02649139 1.1847186 ], Done: False, Info: {}</span><br><span>Action: 1, Reward: 1.0, Next State: [-0.03368816 -0.60167676 0.05018577 0.90045595], Done: False, Info: {}</span><br><span>Action: 0, Reward: 1.0, Next State: [-0.04572169 -0.7974415 0.06819488 1.2084818 ], Done: False, Info: {}</span><br><span>Action: 1, Reward: 1.0, Next State: [-0.06167053 -0.60326344 0.09236452 0.9379254 ], Done: False, Info: {}</span><br><span>Action: 1, Reward: 1.0, Next State: [-0.0737358 -0.40950003 0.11112303 0.6756358 ], Done: False, Info: {}</span><br><span>Action: 0, Reward: 1.0, Next State: [-0.08192579 -0.6059764 0.12463574 1.0011356 ], Done: False, Info: {}</span><br><span>Action: 1, Reward: 1.0, Next State: [-0.09404532 -0.4127204 0.14465846 0.75004834], Done: False, Info: {}</span></pre><h3><span>Explanation:</span></h3><ul><li value="1"><b><strong>Environment Setup: </strong></b><span>We load the CartPole environment with gym.make and specify render_mode=”human” to visualize the environment.</span></li><li value="2"><b><strong>State and Action Spaces: </strong></b><span>We print the state space and action space to understand the dimensions and types of actions available.</span></li><li value="3"><b><strong>Random Actions: </strong></b><span>The agent takes random actions for a few steps, and the state transitions, rewards, and other information are printed for each step.</span></li><li value="4"><b><strong>Termination Handling:</strong></b><span> If an episode ends (i.e., the pole falls), the environment is reset to start a new episode.</span></li></ul><h2><b><strong>Application of Reinforcement Learnings</strong></b><span> </span></h2><p dir="ltr"><b><strong>i) Robotics: </strong></b><span>Automating tasks in structured environments like manufacturing.</span></p> <p dir="ltr"><b><strong>ii) Game Playing: </strong></b><span>Developing strategies in complex games like chess.</span></p> <p dir="ltr"><b><strong>iii) Industrial Control:</strong></b><span> Real-time adjustments in operations like refinery controls.</span></p><div style="text-align:center;margin:20px 0; max-height: 280px;" id="GFG_AD_gfg_outstream_incontent"></div> <p dir="ltr"><b><strong>iv) Personalized Training Systems:</strong></b><span> Customizing instruction based on individual needs.</span></p> <h2><span>Advantages and Disadvantages of Reinforcement Learning</span></h2><p dir="ltr"><b><strong>Advantages:</strong></b></p> <p dir="ltr"><span>1. Reinforcement learning can be used to solve very complex problems that cannot be solved by conventional techniques.</span></p> <p dir="ltr"><span>2. The model can correct the errors that occurred during the training process. </span></p> <p dir="ltr"><span>3. In RL, training data is obtained via the direct interaction of the agent with the environment</span></p> <p dir="ltr"><span>4. Reinforcement learning can handle environments that are non-deterministic, meaning that the outcomes of actions are not always predictable. This is useful in real-world applications where the environment may change over time or is uncertain.</span></p> <p dir="ltr"><span>5. Reinforcement learning can be used to solve a wide range of problems, including those that involve decision making, control, and optimization.</span></p> <p dir="ltr"><span>6. Reinforcement learning is a flexible approach that can be combined with other machine learning techniques, such as deep learning, to improve performance.</span></p> <p dir="ltr"><b><strong>Disadvantages:</strong></b></p> <p dir="ltr"><span>1. Reinforcement learning is not preferable to use for solving simple problems.</span></p> <p dir="ltr"><span>2. Reinforcement learning needs a lot of data and a lot of computation</span></p> <p dir="ltr"><span>3. Reinforcement learning is highly dependent on the quality of the reward function. If the reward function is poorly designed, the agent may not learn the desired behavior.</span></p> <p dir="ltr"><span>4. Reinforcement learning can be difficult to debug and interpret. It is not always clear why the agent is behaving in a certain way, which can make it difficult to diagnose and fix problems.</span></p> <h2><span>Conclusion</span></h2><p dir="ltr" style="text-align: justify;"><span>Reinforcement learning is a powerful technique for decision-making and optimization in dynamic environments. Its applications range from robotics to personalized learning systems. However, the complexity of RL requires careful design of reward functions and significant computational resources. By understanding its principles and applications, one can leverage RL to solve intricate real-world problems.</span></p> <blockquote><p dir="ltr"><b><strong>You can also read our recent article on Implementation – </strong></b><a href="https://www.geeksforgeeks.org/ml-reinforcement-learning-algorithm-python-implementation-using-q-learning/" target="_blank" rel="noopener"><b><strong>Reinforcement Learning Algorithm</strong></b></a><b><strong> </strong></b></p> </blockquote><br/><div id="AP_G4GR_6"></div>  <div class='article_bottom_text'></div><br> <div id="video-tab-content" class="video-tab-content"> <div style="text-align: center; margin: 20px 0px;" id="GFG_AD_InContent_Desktop_728x280"></div> </div> <div class="d-row content-bw article-pgnavi v-divider-gfg"style="margin-top: 20px;"> <div class="article-pgnavi_prev"><div class="comment_div" data-title="Comments"><button class="author_footer_btn" data-gfg-action="loadComments"> <div class='discussion_panel'> <i class='discussion_button'> </i> Comment </div></button></div><div class="trigger-div"><button class="author_footer_btn"><div class="more_info">More info<span class="more_info_uparrow gfg-icon gfg-icon_arrow-down-thin"></span></div></button></div> <a href="https://www.geeksforgeeks.org/about/contact-us/?listicles" target="_blank" class="advertise-with-us-div"><button class="advertise-with-us-btn"><span class="advertise-with-us-text">Advertise with us</span></button></a> </div> <div class="article-pgnavi_next"> <a href="https://www.geeksforgeeks.org/self-supervised-learning-ssl/?ref=next_article" class="pg-head"> <span>Next Article</span> <span class="gfg-icon gfg-icon_next"></span> </a>  <div class="pg-main"> <a href="https://www.geeksforgeeks.org/self-supervised-learning-ssl/?ref=next_article">Self-Supervised Learning (SSL)</a> </div> </div> </div> <div class="more-info hidden-div"> <div class="article-meta-author-details "> <div class="article-meta-author-details-block"> <div class="article-meta-author-details-profile-display"> <div class="author_info"> <div class="article-meta-author-details-profile-display-icon"> <a href="https://www.geeksforgeeks.org/user/Prateek Bajaj/contributions/?itm_source=geeksforgeeks&itm_medium=article_author&itm_campaign=auth_user" style="text-decoration: none;"><div class="image-wrap" style="position: relative"><p class="profileCard-profile-picture" style="background-color:#E5F9DB; 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--experience-sidebar: #000; --left-bar-background: #F0F3F5; } .popup-main { padding: 20px 18px 20px 18px; border-radius: 8px; } .popup-main .popup-heading { display: flex; align-items: center; color: #E9E9EA; margin-bottom: 10px; justify-content: space-between; } .popup-main .close-icon { background-image: var(--com-extra-icons-mobile-image);background-position: -15px -1651px;height: 22px;width: 22px;transform: scale(1); margin-bottom: 12px; cursor: pointer; } .popup-main .experience-card { color: #000; display: grid; grid-template-columns: repeat(2, 1fr); gap: 16px; /* margin-bottom:10px; */ } .popup-main a:hover, .popup-main a:active, .popup-main a:visited { color: inherit; text-decoration: none; } .popup-main .exp-card1, .popup-main .exp-card2, .popup-main .exp-card3, .popup-main .exp-card4, .popup-main .exp-card5, .popup-main .exp-card6 { display: flex; align-items: center; border-radius: 10px; cursor: pointer; } .popup-main .exp-card1 .icon1, .popup-main .exp-card2 .icon2, .popup-main .exp-card3 .icon3, .popup-main .exp-card4 .icon4, .popup-main .exp-card5 .icon5, .popup-main .exp-card6 .icon6 { width: 80px; height: 80px; border-radius: 10px 0px 0px 10px; display: flex; align-items: center; justify-content: center; background: var(--write-experience-card-icon-1); } .popup-main .exp-card1 .icon1, .popup-main .exp-card2 .icon2, .popup-main .exp-card3 .icon3, .popup-main .exp-card4 .icon4, .popup-main .exp-card5 .icon5, .popup-main .exp-card6 .icon6 { width: 80px; height: 80px; border-radius: 10px 0px 0px 10px; display: flex; align-items: center; justify-content: center; } .popup-main .exp-card1 .icon1 { background: var(--write-experience-card-icon-1); } .popup-main .exp-card2 .icon2 { background: var(--write-experience-card-icon-2); } .popup-main .exp-card3 .icon3 { background: var(--write-experience-card-icon-3); } .popup-main .exp-card4 .icon4 { background: var(--write-experience-card-icon-4); } .popup-main .exp-card5 .icon5 { background: var(--write-experience-card-icon-5); } .popup-main .exp-card6 .icon6 { background: var(--write-experience-card-icon-6); } .popup-main .exp-card6 .icon6-image { background-image: url(https://media.geeksforgeeks.org/auth-dashboard-uploads/compass.svg); width: 46px; height: 30px; background-size: 30px; background-repeat: no-repeat; background-position: center; } .popup-main .exp-card1 .icon1-image { background-image: var(--com-extra-icons-mobile-image); width: 46px; height: 28px; background-position: -4px -812px; } .popup-main .exp-card2 .icon2-image { background-image: var(--com-extra-icons-mobile-image); width: 46px; height: 28px; background-position: -4px -888px; } .popup-main .exp-card3 .icon3-image { background-image: var(--com-extra-icons-mobile-image); width: 46px; height: 28px; background-position: -4px -848px; } .popup-main .exp-card4 .icon4-image { background-image: url(https://media.geeksforgeeks.org/auth-dashboard-uploads/competitive.svg); width: 46px; height: 30px; background-size: 30px; background-repeat: no-repeat; background-position: center; } .popup-main .exp-card5 .icon5-image { background-image: var(--com-extra-icons-mobile-image); width: 46px; height: 33px; background-position: -4px -1217px; } .popup-main .exp-card1 .exp-card1-text, .popup-main .exp-card2 .exp-card2-text, .popup-main .exp-card3 .exp-card3-text, .popup-main .exp-card4 .exp-card4-text, .popup-main .exp-card5 .exp-card5-text, .popup-main .exp-card6 .exp-card6-text { display: flex; align-items: center; border-radius: 0px 10px 10px 0px; height: 80px; width: calc(100% - 80px); } .popup-main .exp-card1 { justify-content: flex-start; background: var(--write-experience-card-1); } .popup-main .exp-card2 { justify-content: flex-start; background: var(--write-experience-card-2); } .popup-main .exp-card3 { justify-content: flex-start; background: var(--write-experience-card-3); } .popup-main .exp-card4 { justify-content: flex-start; background: var(--write-experience-card-4); } .popup-main .exp-card5 { justify-content: flex-start; background: var(--write-experience-card-5); } .popup-main .exp-card6 { justify-content: flex-start; background: var(--write-experience-card-6); } .popup-main span { font-family: var(--font-primary); font-size: 14px; font-style: normal; font-weight: 500; line-height: normal; padding-left: 5px; padding-right: 5px; } #popup { display: none; background-color: var(--write-modal-background); padding: 20px; text-align: center; position: fixed; top: 50%; left: 50%; transform: translate(-50%, -50%); z-index: 1026; width: min(650px, calc(100% - 20px)); } .link-container-write{ display:flex; flex-direction:column; } .link-container-write > a { margin:7px 0px; } .phrase__container{ color:var(--color-gfg); border-radius: 6px; width: 100%; background-color:var(--write-redirect-container); padding: 15px; text-align: justify; font-size:14px; } .phrase__container:hover { color:var(--color-gfg) !important; background-color:var(--write-redirect-container-hover); } </style> <div class="popup-container"> <div id="popup" accesskey="" class="popup-main"> <div class="popup-heading"> <h2>What kind of Experience do you want to share?</h2> <div class="close-icon share-experience-modal-close"></div> </div>  <div class="experience-card"> <a class="exp-card5" href= "https://write.geeksforgeeks.org/posts-new?cid=e8fc46fe-75e7-4a4b-be3c-0c862d655ed0" target="_blank"> <div class="icon5"> <div class="icon5-image"></div> </div> <div class="exp-card5-text"> <span style="color: #000;">Interview Experiences</span> </div> </a> <a class="exp-card1" href="https://write.geeksforgeeks.org/posts-new?cid=82536bdb-84e6-4661-87c3-e77c3ac04ede" target="_blank"> <div class="icon1"> <div class="icon1-image"></div> </div> <div class="exp-card1-text"> <span style="color: #000;">Admission Experiences</span> </div> </a> <a class="exp-card6" href= "https://write.geeksforgeeks.org/posts-new?cid=5219b0b2-7671-40a0-9bda-503e28a61c31" target="_blank"> <div class="icon6"> <div class="icon6-image"></div> </div> <div class="exp-card6-text"> <span style="color: #000;">Career Journeys</span> </div> </a> <a class="exp-card2" href="https://write.geeksforgeeks.org/posts-new?cid=22ae3354-15b6-4dd4-a5b4-5c7a105b8a8f" target="_blank"> <div class="icon2"> <div class="icon2-image"></div> </div> <div class="exp-card2-text"> <span style="color: #000;">Work Experiences</span> </div> </a> <a class="exp-card3" href= "https://write.geeksforgeeks.org/posts-new?cid=c5e1ac90-9490-440a-a5fa-6180c87ab8ae" target="_blank"> <div class="icon3"> <div class="icon3-image"></div> </div> <div class="exp-card3-text"> <span style="color: #000;">Campus Experiences</span> </div> </a> <a class="exp-card4" href= "https://write.geeksforgeeks.org/posts-new?cid=5ebb8fe9-b980-4891-af07-f2d62a9735f2" target="_blank"> <div class="icon4"> <div class="icon4-image"></div> </div> <div class="exp-card4-text"> <span style="color: #000;">Competitive Exam Experiences</span> </div> </a> </div>  </div> <div id="overlay" onclick="toggleExperiencePopup()" style="display: block;min-height: 100vh;min-width: 100vw;position: fixed;top: 0;bottom: 0;right: 0;left: 0;display: none;justify-content: center;align-items: center;background: rgba(0,0,0,.702);z-index: 1025 !important;backdrop-filter: blur(4px); 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