The art of reinforcement learning : fundamentals, mathematics, and implementations with Python / Michael Hu.
2023
Q325.6
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Title
The art of reinforcement learning : fundamentals, mathematics, and implementations with Python / Michael Hu.
Author
Hu, Michael, author.
ISBN
9781484296066 (electronic bk.)
1484296060 (electronic bk.)
1484296052
9781484296059
1484296060 (electronic bk.)
1484296052
9781484296059
Imprint
New York, NY : Apress, 2023.
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-1-4842-9606-6 doi
Call Number
Q325.6
Dewey Decimal Classification
006.3/1
Summary
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. 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.
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Print version: 9781484296059
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Table of Contents
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.
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.