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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

Values and policy

Before getting to the main subject of this chapter, policy gradients, let’s refresh our minds with the common characteristics of the methods covered in Part 2 of this book. The central topic in value iteration and Q-learning is the value of the state (V s) or value of the state and action (Qs,a). Value is defined as the discounted total reward that we can gather from this state or by issuing this particular action from the state. If we know this quantity, our decision on every step becomes simple and obvious: we just act greedily in terms of value, and that guarantees us a good total reward at the end of the episode. So, the values of states (in the case of the value iteration method) or state + action (in the case of Q-learning) stand between us and the best reward. To obtain these values, we have used the Bellman equation, which expresses the value in the current step via the value in the next step.

In Chapter 1, we defined the entity that...

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