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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

Things to try

In this chapter, we only started playing with MiniWoB++ by looking at some of the easiest environments from the full set of over 100 problems, so there is plenty of uncharted territory ahead. If you want to practice, there are several items you can experiment with:

  • Testing the robustness of demonstrations to noisy clicks.

  • The action space for the clicking approach could be improved by predicting the x and y coordinates of the place to click.

  • DOM data could be used instead of (or in addition to) screen pixels. Then, the prediction will be the element of the tree to be clicked.

  • Try other problems. There is a wide variety of them, requiring keyboard events to be generated, the sequence of actions planned, etc.

  • Very recently, the LaVague project was published (https://github.com/lavague-ai/LaVague), which uses LLMs for web automation...

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