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 theoretical foundations of RL

In this section, I will introduce you to the mathematical representation and notation of the formalisms (reward, agent, actions, observations, and environment) that we just discussed. Then, using this as a knowledge base, we will explore the second-order notions of the RL language, including state, episode, history, value, and gain, which will be used repeatedly to describe different methods later in the book.

Markov decision processes

Before that, we will cover Markov decision processes (MDPs), which will bedescribed like a Russian matryoshka doll: we will start from the simplest case of a Markov process (MP), then extend that with rewards, which will turn it into a Markov reward process (MRP). Then, we will put this idea into an extra envelope by adding actions, which will lead us to an MDP.

MPs and MDPs are widely used in computer science and other engineering fields. So, reading this chapter will be useful for...

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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 theoretical foundations of RL

Markov decision processes

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 theoretical foundations of RL

Markov decision processes

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Authors (1)

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