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001484709 001__ 1484709
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001484709 019__ $$a1413735899$$a1414457182
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001484709 0247_ $$a10.1007/978-1-4842-9606-6$$2doi
001484709 035__ $$aSP(OCoLC)1413735113
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001484709 050_4 $$aQ325.6
001484709 08204 $$a006.3/1$$223/eng/20231219
001484709 1001_ $$aHu, Michael,$$eauthor.
001484709 24514 $$aThe art of reinforcement learning :$$bfundamentals, mathematics, and implementations with Python /$$cMichael Hu.
001484709 260__ $$aNew York, NY :$$bApress,$$c2023.
001484709 300__ $$a1 online resource
001484709 336__ $$atext$$btxt$$2rdacontent
001484709 337__ $$acomputer$$bc$$2rdamedia
001484709 338__ $$aonline resource$$bcr$$2rdacarrier
001484709 5050_ $$aPart I: Foundation -- Chapter 1: Introduction to Reinforcement Learning -- Chapter 2: Markov Decision Processes -- Chapter 3: Dynamic Programming -- Chapter 4: Monte Carlo Methods -- Chapter 5: Temporal Difference Learning -- Part II: Value Function Approximation -- Chapter 6: Linear Value Function Approximation -- Chapter 7: Nonlinear Value Function Approximation -- Chapter 8: Improvement to DQN -- Part III: Policy Approximation -- Chapter 9: Policy Gradient Methods -- Chapter 10: Problems with Continuous Action Space -- Chapter 11: Advanced Policy Gradient Methods -- Part IV: Advanced Topics -- Chapter 12: Distributed Reinforcement Learning -- Chapter 13: Curiosity-Driven Exploration -- Chapter 14: Planning with a Model AlphaZero.
001484709 506__ $$aAccess limited to authorized users.
001484709 520__ $$aUnlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's 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 Learn Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approaches Model problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learning Utilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methods Understand the architecture and advantages of distributed reinforcement learning Master the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agents Explore the AlphaZero algorithm and how it was able to beat professional Go players Who This Book Is For Machine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.
001484709 650_6 $$aApprentissage par renforcement (Intelligence artificielle)
001484709 650_6 $$aSystèmes à réaction.
001484709 650_6 $$aPython (Langage de programmation)
001484709 650_0 $$aReinforcement learning.
001484709 650_0 $$aFeedback control systems.$$0(DLC)sh 85047649
001484709 650_0 $$aPython (Computer program language)$$0(DLC)sh 96008834
001484709 655_0 $$aElectronic books.
001484709 77608 $$iPrint version:$$z1484296052$$z9781484296059$$w(OCoLC)1380391041
001484709 852__ $$bebk
001484709 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-9606-6$$zOnline Access$$91397441.1
001484709 909CO $$ooai:library.usi.edu:1484709$$pGLOBAL_SET
001484709 980__ $$aBIB
001484709 980__ $$aEBOOK
001484709 982__ $$aEbook
001484709 983__ $$aOnline
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