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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

Basic DQN

To get started, we will implement the same DQN method as in Chapter 6, but leveraging the high-level primitives described in Chapter 7. This will make our code much more compact, which is good, as non-relevant details won’t distract us from the method’s logic. At the same time, the purpose of this book is not to teach you how to use the existing libraries but rather how to develop intuition about RL methods and, if necessary, implement everything from scratch. From my perspective, this is a much more valuable skill, as libraries come and go, but true understanding of the domain will allow you to quickly make sense of other people’s code and apply it consciously.

In the basic DQN implementation, we have three modules in the Chapter08 folder of the GitHub repository for this book:

  • Chapter08/lib/dqn_model.py: The DQN neural network (NN), which is the same as in Chapter 6, so I won’t repeat it

    ...
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