The Art of Reinforcement Learning 1st Edition Michael Hu

The Art of Reinforcement Learning – Ebook PDF Instant Download/Delivery

Product details:

• ISBN-10 ‏ : ‎ 1484296052
• ISBN-13 ‏ : ‎ 978-1484296059
• Author(s): Michael Hu

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).

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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