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Deep Reinforcement Learning and Its Industrial Use Cases

2024-10-01 Computers
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Release : 2024-10-01
Genre : Computers
Kind : eBook
Book Rating : 561/5 ( reviews)

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Book Synopsis Deep Reinforcement Learning and Its Industrial Use Cases by : Shubham Mahajan

Download or read book Deep Reinforcement Learning and Its Industrial Use Cases written by Shubham Mahajan. This book was released on 2024-10-01. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. “Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications” is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you’re an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency.

Reinforcement Learning

2020-11-06 Computers
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Release : 2020-11-06
Genre : Computers
Kind : eBook
Book Rating : 346/5 ( reviews)

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Book Synopsis Reinforcement Learning by : Phil Winder Ph.D.

Download or read book Reinforcement Learning written by Phil Winder Ph.D.. This book was released on 2020-11-06. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

Deep Reinforcement Learning and Its Industrial Use Cases

2024-10-29 Computers
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Release : 2024-10-29
Genre : Computers
Kind : eBook
Book Rating : 553/5 ( reviews)

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Book Synopsis Deep Reinforcement Learning and Its Industrial Use Cases by : Shubham Mahajan

Download or read book Deep Reinforcement Learning and Its Industrial Use Cases written by Shubham Mahajan. This book was released on 2024-10-29. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premise—enabling machines to learn optimal actions within complex environments through trial and error—has broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. “Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications” is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you’re an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency.

The The Reinforcement Learning Workshop

2020-08-18 Computers
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Release : 2020-08-18
Genre : Computers
Kind : eBook
Book Rating : 967/5 ( reviews)

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Book Synopsis The The Reinforcement Learning Workshop by : Alessandro Palmas

Download or read book The The Reinforcement Learning Workshop written by Alessandro Palmas. This book was released on 2020-08-18. Available in PDF, EPUB and Kindle. Book excerpt: Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

Deep Reinforcement Learning

2023
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Release : 2023
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Book Synopsis Deep Reinforcement Learning by : Asiri Iroshan

Download or read book Deep Reinforcement Learning written by Asiri Iroshan. This book was released on 2023. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is a type of machine learning that has attracted a lot of attention in recent years because to its incredible achievements in a variety of applications like pattern recognition, audio recognition, computer vision, and natural language processing. Deep learning approaches can also be used with reinforcement learning methods to create effective representations for situations with high dimensional raw data input, according to recent study. Deep Reinforcement Learning has made it possible to learn policies for complicated tasks in partially observable settings without having to master the tasks' underlying model. Production systems face significant problems as a result of shorter product development cycles and fully customizable goods. These are required to handle not only a greater variety of products but also high throughputs, high flexibility, and resistance to process changes and unforeseen catastrophes. Deep Reinforcement Learning (RL) has been used more and more for production system optimization to address these issues. In Deep RL, recently gathered sensor-data are utilized unlike conventional Machine Learning (ML) techniques enabling real-time responses to the changes in the system. Although deep RL is now being used in production systems, it has not yet been possible to conduct a thorough analysis of the outcomes. This paper's main contribution is to give researchers and practitioners an overview of relevant applications and to inspire additional deep RL enabled production system implementations and research. The results show that deep RL is used in a range of industrial domains, supporting flexible and data-driven operations. In the majority of applications, traditional approaches performed better, requiring less effort to deploy or relying less on human expertise. However, in order to analyse safety concerns and establish reliability under real-world settings, future research needs concentrate more on applying the findings to practical systems. This article examines the applications of Deep Reinforcement Learning and its recent breakthroughs, focusing on the most commonly used deep architectures in relevance to Industrial Automation and Production Systems.

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