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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

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (21) Chapters Close

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 Other Books You May Enjoy 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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