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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

Summary

In this chapter, we just touched a bit on the very interesting and dynamic field of MARL, which has several practical applications in trading simulation, communication networks, and others. There are lots of things that you can try on your own using the MAgent environment or other environments (like PySC2).

My congratulations on reaching the end of the book! I hope that the book was useful and you enjoyed reading it as much as I enjoyed gathering the material and writing all the chapters. As a final word, I would like to wish you good luck in this exciting and dynamic area of RL. The domain is developing very rapidly, but with an understanding of the basics, it will become much simpler for you to keep track of the new developments and research in this field.

There are many very interesting topics left uncovered, such as partially observable Markov decision processes (where environment observations don’t fulfill the Markov property) or recent approaches...

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