The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9781484296059,1484296052,9781484296066, 1484296060

Product details:

• ISBN-10: 1484296060
• ISBN-13: 9781484296066
• Author: Michael Hu

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.

Table contents:

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

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