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The art of reinforcement learning = fundamentals, mathematics, and implementations with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The art of reinforcement learning/ by Michael Hu.
Reminder of title:	fundamentals, mathematics, and implementations with Python /
Author:	Hu, Michael.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xvii, 287 p. :ill., digital ;24 cm.
[NT 15003449]:	Part 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.
Contained By:	Springer Nature eBook
Subject:	Reinforcement learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-9606-6
ISBN:	9781484296066

The art of reinforcement learning = fundamentals, mathematics, and implementations with Python /
Hu, Michael.

The art of reinforcement learningfundamentals, mathematics, and implementations with Python /[electronic resource] :by Michael Hu. - Berkeley, CA :Apress :2023. - xvii, 287 p. :ill., digital ;24 cm.

Part 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.

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 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.

ISBN: 9781484296066

Standard No.: 10.1007/978-1-4842-9606-6doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .H82 2023

Dewey Class. No.: 006.31

The art of reinforcement learning = fundamentals, mathematics, and implementations with Python /
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245 1 4 $a The art of reinforcement learning $h [electronic resource] : $b fundamentals, mathematics, and implementations with Python / $c by Michael Hu.
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505 0 $a Part 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.
520 $a 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 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.
650 0 $a Reinforcement learning. $3 1006373
650 0 $a Feedback control systems. $3 531860
650 0 $a Python (Computer program language) $3 729789
650 2 4 $a Python. $3 3201289
650 2 4 $a Artificial Intelligence. $3 769149
650 1 4 $a Machine Learning. $3 3382522
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