The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementation

Description: The Art of Reinforcement Learning by Michael Hu Estimated delivery 3-12 business days Format Paperback Condition Brand New Description Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RLs core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology.Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO).This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques.With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students.What You Will LearnGrasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approachesModel problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learningUtilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methodsUnderstand the architecture and advantages of distributed reinforcement learningMaster the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agentsExplore the AlphaZero algorithm and how it was able to beat professional Go players Who This Book Is ForMachine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications. Author Biography Michael Hu is a skilled software engineer with over a decade of experience in designing and implementing enterprise-level applications. Hes a passionate coder who loves to delve into the world of mathematics and has a keen interest in cutting-edge technologies like machine learning and deep learning, with a particular interest in deep reinforcement learning. He has build various open-source projects on Github, which closely mimic the state-of-the-art reinforcement learning algorithms developed by DeepMind, such as AlphaZero, MuZero, and Agent57. Fluent in both English and Chinese, Michael currently resides in the bustling city of Shanghai, China. Details ISBN 1484296052 ISBN-13 9781484296059 Title The Art of Reinforcement Learning Author Michael Hu Format Paperback Year 2023 Pages 287 Edition 1st Publisher APress GE_Item_ID:157709140; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys

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End Time: 2024-10-25T03:19:12.000Z

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