The Art of Reinforcement Learning : Fundamentals, Mathematics, and Implementations with Python / by Michael Hu.

Format:

Book

Author/Creator:

Hu, Michael.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Artificial intelligence.

Machine Learning.

Python.

Artificial Intelligence.

Local Subjects:

Machine Learning.

Python.

Artificial Intelligence.

Physical Description:

1 online resource (290 pages)

Edition:

1st ed. 2023.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2023.

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. 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.

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.

Notes:

Includes index.

ISBN:

9781484296066

1484296060

OCLC:

1413735113