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

View More author details

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

The first training objective

Let's now discuss what we want our robot to do and how we're going to get there. It's not very hard to notice that the potential capabilities of the hardware described are quite limited:

We have only four servos with a constrained angle of rotation: This makes our robot's movements highly dependent on friction with the surface, as it can't bring its individual legs up, which is also the case with the Minitaur robot, which has two motors attached to every leg.
Our hardware capacity is small: The memory is limited, the central processing unit (CPU) is not very fast, and no hardware accelerators are present. In the subsequent sections, we will take a look at how to deal with those limitations to some extent.
We have no external connectivity besides a micro-USB port: Some boards might have Wi-Fi hardware, which could be used to offload the NN inference to a larger machine, but in this chapter's example, I'm...