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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835882702
Length 716 pages
Edition 3rd Edition
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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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Toc

Table of Contents (29) Chapters Close

Preface 1. Part 1 Introduction to RL FREE CHAPTER
2. What Is Reinforcement Learning? 3. OpenAI Gym API and Gymnasium 4. Deep Learning with PyTorch 5. The Cross-Entropy Method 6. Part 2 Value-based methods
7. Tabular Learning and the Bellman Equation 8. Deep Q-Networks 9. Higher-Level RL Libraries 10. DQN Extensions 11. Ways to Speed Up RL 12. Stocks Trading Using RL 13. Part 3 Policy-based methods
14. Policy Gradients 15. Actor-Critic Method: A2C and A3C 16. The TextWorld Environment 17. Web Navigation 18. Part 4 Advanced RL
19. Continous Action Space 20. Trust Region Methods 21. Black-Box Optimizations in RL 22. Advanced Exploration 23. Reinforcement Learning with Human Feedback 24. AlphaGo Zero and MuZero 25. RL in Discrete Optimization 26. Multi-Agent RL 27. Bibliography
28. Index

Browser automation and RL

For a long time, automating website interaction focused on the very practical tasks of website testing and web scraping. Website testing is especially critical when you have a complicated website that you (or other people) have developed and you want to ensure that it does what it is supposed to do. For example, if you have a login page that has been redesigned and is ready to be deployed on a live website, then you will want to be sure that this new design does sane things in case a wrong password is entered, the user clicks on I forgot my password, and so on. A complex website could potentially include hundreds or thousands of use cases that should be tested on every release, so all such functions should be automated.

Web scraping solves the problem of extracting data from websites at scale. For example, if you want to build a system that aggregates all prices for all the pizza places in your town, you will potentially need to deal with hundreds...

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