The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python 1st Edition Michael Hu

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

Product details:

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

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.

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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