Grokking Deep Reinforcement Learning
Miguel Morales
  • MEAP began May 2018
  • Publication in April 2019 (estimated)
  • ISBN 9781617295454
  • 450 pages (estimated)
  • printed in black & white

The must-have book, for anyone that wants to have a profound understanding of deep reinforcement learning.

Julien Pohie

We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment.

Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

Table of Contents detailed table of contents

Part 1: Reinforcement Learning Foundations

1. Introduction to Deep Reinforcement Learning

1.1. What is Deep Reinforcement Learning?

1.2. Why now?

1.3. When to use Deep Reinforcement Learning?

1.4. What will you learn in this book?

1.5. What do you need?

1.6. Summary

2. Planning For Sequential Decision-Making Problems

2.1. Architecture of a sequential decision-making problem

2.2. Objective of a decision-making agent

2.3. Planning optimal sequences of actions

2.4. Summary

3. Learning to Act Through Interaction

3.1. The challenge of interpreting evaluative feedback

3.2. Learning to estimate policies

3.3. Learning to optimize behavior

3.4. Summary

4. More Effective and Efficient Reinforcement Learning

4.1. Strategic exploration

4.2. Agents that that learn often and accurately

4.3. Agents that interact, learn and plan

4.4. Summary

Part 2: Deep Reinforcement Learning Algorithms

5. Introduction to value-based deep reinforcement learning

5.1. The kind of feedback a deep reinforcement learningagent deals with

5.2. Introduction to value-function approximation

5.3. NFQ: A first attempt to value-based deep reinforcementlearning

5.4. Summary

6. Stabilizing value-based deep reinforcement learning method

6.1. DQN: Making reinforcement learning more like supervised learning

6.2. Double DQN: Mitigating the overestimation of approximateaction-value functions

6.3. Summary

7. Actor-Critic Methods

8. Gradient-Free Methods

Part 3: Advanced Applications

9. Advanced Exploration Strategies

10. Reinforcement Learning in Robots

11. Reinforcement Learning with Multiple Agents

12. Towards Artificial General Intelligence

About the book

Grokking Deep Reinforcement Learning is a beautifully balanced approach to teaching, offering numerous large and small examples, annotated diagrams and code, engaging exercises, and skillfully crafted writing. You'll explore, discover, and learn as you lock in the ins and outs of reinforcement learning, neural networks, and AI agents. You will go from small grid world environments and some of the foundational algorithms to some of the most challenging environments out there today and cutting-edge techniques to solve these environments.

Exciting, fun, and maybe even a little dangerous. Let's get started!


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