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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

Other RL libraries

As we discussed earlier, there are several RL-specific libraries available. A few years ago, TensorFlow was more popular than PyTorch, but nowadays, PyTorch is dominating the field, and there is a recent trend of JAX being used as it provides better performance. The following is my recommended list of libraries you might want to take into consideration for your projects:

  • stable-baselines3: We mentioned this library when we discussed Atari wrappers. This is a fork of the OpenAI Stable Baselines repository, and the main idea is to have an optimized and reproducible set of RL algorithms that you can use to check your methods ( https://github.com/DLR-RM/stable-baselines3).

  • TorchRL: RL extensions for PyTorch. This library is relatively young-—the first release was at the end of 2022—but provides rich set of helper classes for RL. Its design philosophy is very close to PTAN—a Python-first set...

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