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Deep Reinforcement Learning Hands-On

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

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Length 826 pages
Edition 2nd Edition
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (28) Chapters Close

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...

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