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

Human demonstrations

In order to improve the training process, let’s try to incorporate human demonstrations. The idea behind demonstrations is simple: to help our agent to discover the best way to solve the task, we show it some examples of actions that we think are required for the problem. Those examples might not be the best solution or not 100% accurate, but they should be good enough to show the agent promising directions to explore.

In fact, this is a very natural thing to do, as all human learning is based on some prior examples given by a teacher in class, parents, or other people. Those examples could be in a written form (for example, recipe books) or given as demonstrations that you need to repeat several times to get right (for example, dance classes). Such forms of training are much more effective than random searches. Just imagine how complicated and lengthy it would be to learn how to clean your teeth by trial and error alone. Of course, there is 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

Human demonstrations

Authors (1)

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Table of Contents (29) Chapters

Human demonstrations

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