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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

Real-life value iteration

The improvements that we got in the FrozenLake environment by switching from the cross-entropy method to the value iteration method are quite encouraging, so it’s tempting to apply the value iteration method to more challenging problems. However, it is important to look at the assumptions and limitations that our value iteration method has. But let’s start with a quick recap of the method. On every step, the value iteration method does a loop on all states, and for every state, it performs an update of its value with a Bellman approximation. The variation of the same method for Q-values (values for actions) is almost the same, but we approximate and store values for every state and action. So what’s wrong with this process?

The first obvious problem is the count of environment states and our ability to iterate over them. In value iteration, we assume that we know all states in our environment in advance, can iterate over them...

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