You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Length 826 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Chatbots

Author (1):

Maxim Lapan

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Table of Contents (28) Chapters

Preface

1. What Is Reinforcement Learning?

2. OpenAI Gym FREE CHAPTER

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. Higher-Level RL Libraries

8. DQN Extensions

9. Ways to Speed up RL

10. Stocks Trading Using RL

11. Policy Gradients – an Alternative

12. The Actor-Critic Method

13. Asynchronous Advantage Actor-Critic

14. Training Chatbots with RL

15. The TextWorld Environment

16. Web Navigation

17. Continuous Action Space

18. RL in Robotics

19. Trust Regions – PPO, TRPO, ACKTR, and SAC

20. Black-Box Optimization in RL

21. Advanced Exploration

22. Beyond Model-Free – Imagination

23. AlphaGo Zero

24. RL in Discrete Optimization

25. Multi-agent RL

26. Other Books You May Enjoy

27. Index

Model-based methods

To begin, let's discuss the difference between the model-free approach that we have used in the book and model-based methods, including their strong and weak points and where they might be applicable.

Model-based versus model-free

In the The taxonomy of RL methods section in Chapter 4, The Cross-Entropy Method, we saw several different angles from which we can classify RL methods. We distinguished three main aspects:

Value-based and policy-based
On-policy and off-policy
Model-free and model-based

There were enough examples of methods on both sides of the first and second categories, but all the methods that we have covered so far were 100% model-free. However, this doesn't mean that model-free methods are more important or better than their model-based counterparts. Historically, due to their sample efficiency, the model-based methods have been used in the robotics field and other industrial controls. This has also happened...