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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

Thanks for reaching the end! I hope you enjoyed reading this chapter as much as I enjoyed writing it. This field is very interesting; we have just touched on it a little, but I hope that this chapter will show you a direction for your own experiments and projects. The goal of the chapter wasn't building a robot that will stand, as this could be done in a much easier and more efficient way; the true goal was to show how the RL way of thinking can be applied to robotics problems, and how you can do your own experiments with real hardware without having access to expensive robotic arms, complex robots, and so on.

At the same time, I see some potential for the RL approach to be applied to complex robots, and who knows, maybe you will build the next version of iRobot Corporation to bring more robots into our lives. If you are interested in buying the kits for the robot platform described in this chapter, it would be really helpful if you could fill out this form: https:...

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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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