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 Cross-Entropy Method

In the last chapter, you got to know PyTorch. In this chapter, we will wrap up part one of this book and you will become familiar with one of the reinforcement learning (RL) methods: cross-entropy.

Despite the fact that it is much less famous than other tools in the RL practitioner's toolbox, such as deep Q-network (DQN) or advantage actor-critic, the cross-entropy method has its own strengths. Firstly, the cross-entropy method is really simple, which makes it an easy method to follow. For example, its implementation on PyTorch is less than 100 lines of code.

Secondly, the method has good convergence. In simple environments that don't require complex, multistep policies to be learned and discovered, and that have short episodes with frequent rewards, the cross-entropy method usually works very well. Of course, lots of practical problems don't fall into this category, but sometimes they do. In such cases, the cross-entropy method (on its...