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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

Chatbot training

Natural language understanding was the stuff of science fiction for a long time. In science fiction, you can just chat with your starship's computer to get useful and relevant information about the recent alien invasion, without pressing any button. This scenario was exploited by authors and filmmakers for decades, but in real life, such interactions with computers started to become a reality only recently. You still can't talk to your starship, but you can, at least, switch your toaster on and off without pushing buttons, which is undoubtedly a major step forward!

The reason why it took computers so long to understand language is simply due to the complexity of language itself. Even in trivial scenarios—like saying, "Toaster, switch on!"—you can imagine several ways to formulate your command, and it's usually very hard to capture all those ways and corner cases in advance using normal computer programming techniques. Unfortunately...

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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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