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

Product type Paperback

Published in Nov 2024

Publisher Packt

ISBN-13 9781835882702

Length 716 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

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Table of Contents (29) Chapters

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

The deep NLP basics

In this short section, I’m going to walk you through deep NLP building blocks and standard approaches. This domain is evolving at enormous speed, especially now, as ChatGPT and LLMs have set a new standards in chatbots and text processing.

The material in this section just scratches the surface and covers the most common and standard building blocks. Some of them, like RNNs and LSTMs, might even look outdated — I still believe this is fine, as being aware of historical perspective is important. For simple tasks, you might consider using simple tools depending on what is most suitable for the task at hand, even if they are not hyped anymore.

Recurrent Neural Networks (RNNs)

NLP has its own specifics that make it different from computer vision or other domains. One such feature is processing variable-length objects. At various levels, NLP deals with objects that could have different lengths; for example, a word in a...

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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

Table of Contents (29) Chapters

The deep NLP basics

Recurrent Neural Networks (RNNs)

Authors (1)

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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

Table of Contents (29) Chapters

The deep NLP basics

Recurrent Neural Networks (RNNs)

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Authors (1)

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