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Deep Reinforcement Learning Hands-On

You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788834247

Length 546 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

View More author details

Table of Contents (21) Chapters

Preface

1. What is Reinforcement Learning? FREE CHAPTER

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. DQN Extensions

8. Stocks Trading Using RL

9. Policy Gradients – An Alternative

10. The Actor-Critic Method

11. Asynchronous Advantage Actor-Critic

12. Chatbots Training with RL

13. Web Navigation

14. Continuous Action Space

15. Trust Regions – TRPO, PPO, and ACKTR

16. Black-Box Optimization in RL

17. Beyond Model-Free – Imagination

18. AlphaGo Zero

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Index

Book summary

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 material and writing all the chapters. As a final word, I'd 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 becomes much simpler for you to keep track of the new developments and research in this field.

There are lots of very interesting topics left uncovered, such as partially observable MDPs (where environment observations don't fulfill the Markov property) or recent approaches to exploration, such as the count-based methods. There is a lot of recent activity around multi-agent methods, where many agents need to learn how to coordinate to solve a common problem. We also haven't mentioned the memory-based RL approach, where your agent can maintain some sort of a memory to keep its knowledge and experience...

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Authors (1)

Maxim Lapan

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.

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