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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

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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

Web navigation

When the web was invented, it started as several text-only web pages interconnected by hyperlinks. If you're curious, here is the first web page home: http://info.cern.ch/, with text and links. The only thing you can do is to read and click on links to go between pages. Several years later, in 1995, IETF published HTML 2.0 specification and it had lots of extensions to the original version invented by Tim Berners-Lee. Among these extensions it included forms and form elements that allowed web page authors to add activity to their websites. Users could enter and change text, toggle checkboxes, select drop-down lists, and push buttons. The set of controls was similar to the minimalistic set of GUI application's controls. There was one single difference: all this happened inside the browser's window and both the data and UI controls that users interacted with were defined by the server's page, but not by the local application installed.

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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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