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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

You have started to learn about the practical side of RL! In this chapter, we installed OpenAI Gym, with its tons of environments to play with. We studied its basic API and created a randomly behaving agent.

You also learned how to extend the functionality of existing environments in a modular way and became familiar with a way to record our agent's activity using the Monitor class. This will be heavily used in the upcoming chapters.

In the next chapter, we will do a quick DL recap using PyTorch, which is a favorite library among DL researchers. Stay tuned.

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020 Publisher: Packt ISBN-13: 9781838826994

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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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