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دانلود کتاب The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python
دانلود کتاب هنر تقویت یادگیری: مبانی، ریاضیات و پیاده سازی با پایتون
مشخصات کتاب
The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python
ویرایش:
نویسندگان: Michael Hu
سری:
ISBN (شابک) : 9781484296066, 9781484296059
ناشر: Apress
سال نشر: 2023
تعداد صفحات: 290
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 19 Mb
قیمت کتاب (تومان) : 37,000
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توضیحاتی در مورد کتاب هنر تقویت یادگیری: مبانی، ریاضیات و پیاده سازی با پایتون
توضیحاتی درمورد کتاب به خارجی
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.
فهرست مطالب
Cover Front Matter Part I. Foundation 1. Introduction 2. Markov Decision Processes 3. Dynamic Programming 4. Monte Carlo Methods 5. Temporal Difference Learning Part II. Value Function Approximation 6. Linear Value Function Approximation 7. Nonlinear Value Function Approximation 8. Improvements to DQN Part III. Policy Approximation 9. Policy Gradient Methods 10. Problems with Continuous Action Space 11. Advanced Policy Gradient Methods Part IV. Advanced Topics 12. Distributed Reinforcement Learning 13. Curiosity-Driven Exploration 14. Planning with a Model: AlphaZero Back Matter
