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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Pages 826 pages

Edition 2nd Edition

Languages

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

Table of Contents (28) Chapters

Preface

1. What Is Reinforcement Learning?

2. OpenAI Gym

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

Summary

In this chapter, we just touched a bit on the very interesting and dynamic field of MARL. There are lots of things that you can try on your own using the MAgent environment or other environments (like PySC2).

My congratulations on reaching the end of the book! I hope that the book was useful and you enjoyed reading it as much as I enjoyed gathering the material and writing all the chapters. As a final word, I would like to wish you good luck in this exciting and dynamic area of RL. The domain is developing very rapidly, but with an understanding of the basics, it will become much simpler for you to keep track of the new developments and research in this field.

There are many very interesting topics left uncovered, such as partially observable Markov decision processes (where environment observations don't fulfill the Markov property) or recent approaches to exploration, such as the count-based methods. There has been a lot of recent activity around multi-agent methods...

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Authors (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

See other products by Maxim Lapan

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