LEADER 04798nam 22006375 450 001 9910770270703321 005 20251008164946.0 010 $a9781484296066 010 $a1484296060 024 7 $a10.1007/978-1-4842-9606-6 035 $a(MiAaPQ)EBC31009067 035 $a(Au-PeEL)EBL31009067 035 $a(DE-He213)978-1-4842-9606-6 035 $a(OCoLC)1413735113 035 $a(OCoLC-P)1413735113 035 $a(CKB)29337696400041 035 $a(CaSebORM)9781484296066 035 $a(OCoLC)1415898776 035 $a(Perlego)4515707 035 $a(EXLCZ)9929337696400041 100 $a20231208d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Art of Reinforcement Learning $eFundamentals, Mathematics, and Implementations with Python /$fby Michael Hu 205 $a1st ed. 2023. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2023. 215 $a1 online resource (290 pages) 300 $aIncludes index. 311 08$a9781484296059 311 08$a1484296052 327 $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. 330 $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. You will: 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. 606 $aMachine learning 606 $aPython (Computer program language) 606 $aArtificial intelligence 606 $aMachine Learning 606 $aPython 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aPython. 615 24$aArtificial Intelligence. 676 $a006.31 700 $aHu$b Michael$01460730 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910770270703321 996 $aThe Art of Reinforcement Learning$93660698 997 $aUNINA