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

RL in Robotics

This chapter is a bit unusual in comparison to the other chapters in this book for the following reasons:

It took me almost four months to gather all the materials, do the experiments, write the examples, and so on
This is the only chapter in which we will try to step beyond emulated environments into the physical world
In this chapter, we will build a small robot from accessible and cheap components to be controlled using reinforcement learning (RL) methods

This topic is an amazing and fascinating field for many reasons that couldn't be covered in a whole book, much less in a short chapter. So, this chapter doesn't pretend to offer anywhere close to complete coverage of the robotics field. It is just a short introduction that shows what can be done with commodity components, and outlines future directions for your own experiments and research. In addition, I have to admit that I'm not an expert in robotics and have never worked...