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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

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

View More author details

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

Things to try

If you are curious and want to experiment with this chapter’s material on your own, then here is a shortlist of directions to explore. Be warned though: they can take lots of time and may cause you some moments of frustration during your experiments. However, these experiments are a very efficient way to really master the material from a practical point of view:

Try to take some other games from the Atari set, such as Breakout, Atlantis, or River Raid (my childhood favorite). This could require the tuning of hyperparameters.
As an alternative to FrozenLake, there is another tabular environment, Taxi, which emulates a taxi driver who needs to pick up passengers and take them to a destination.
Play with Pong hyperparameters. Is it possible to train faster? OpenAI claims that it can solve Pong in 30 minutes using the asynchronous advantage actor-critic method (which is a subject...

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Authors (1)

Maxim Lapan

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

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