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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

Interactive fiction

As you have already seen, computer games are not only entertaining for humans but also provide challenging problems for RL researchers due to the complicated observations and action spaces, long sequences of decisions to be made during the gameplay, and natural reward systems.

Arcade games like those on the Atari 2600 are just one of many genres that the gaming industry has. Let’s take a step back and take a quick look at the historical perspective. The Atari 2600 platform peaked in popularity during the late 70s and early 80s. Then followed the era of Z80 and clones, which evolved into the period of the PC-compatible platforms and consoles we have now. Over time, computer games continually become more complex, colorful, and detailed in terms of graphics, which inevitably increased hardware requirements. This trend makes it harder for RL researchers and practitioners to apply RL methods to the more recent games; for example, almost everybody can...

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