You're reading from Mastering Reinforcement Learning with Python Build next-generation, self-learning models using reinforcement learning techniques and best practices

Product type Paperback

Published in Dec 2020

Publisher Packt

ISBN-13 9781838644147

Length 544 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Reinforcement Learning

Author (1):

Enes Bilgin

View More author details

Table of Contents (24) Chapters

Preface

1. Section 1: Reinforcement Learning Foundations

2. Chapter 1: Introduction to Reinforcement Learning FREE CHAPTER

3. Chapter 2: Multi-Armed Bandits

4. Chapter 3: Contextual Bandits

5. Chapter 4: Makings of a Markov Decision Process

6. Chapter 5: Solving the Reinforcement Learning Problem

7. Section 2: Deep Reinforcement Learning

8. Chapter 6: Deep Q-Learning at Scale

9. Chapter 7: Policy-Based Methods

10. Chapter 8: Model-Based Methods

11. Chapter 9: Multi-Agent Reinforcement Learning

12. Section 3: Advanced Topics in RL

13. Chapter 10: Introducing Machine Teaching

14. Chapter 11: Achieving Generalization and Overcoming Partial Observability

15. Chapter 12: Meta-Reinforcement Learning

16. Chapter 13: Exploring Advanced Topics

17. Section 4: Applications of RL

18. Chapter 14: Solving Robot Learning

19. Chapter 15: Supply Chain Management

20. Chapter 16: Personalization, Marketing, and Finance

21. Chapter 17: Smart City and Cybersecurity

22. Chapter 18: Challenges and Future Directions in Reinforcement Learning

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Offline reinforcement learning

Offline reinforcement learning is about training agents using data recorded during some prior interactions of an agent (likely non-RL, such as a human agent) with the environment, as opposed to directly interacting with it. It is also called batch reinforcement learning. In this section, we look into some of the key components of offline RL. Let's get started with an overview of how it works.

An overview of how offline reinforcement learning works

In offline RL, the agent does not directly interact with the environment to explore and learn a policy. Figure 13.12 contrasts this to on-policy and off-policy settings.

Figure 13.12 – Comparison of on-policy, off-policy, and offline deep RL (adapted from Levine 2020).

Let's unpack what this figure illustrates:

In on-policy RL, the agent collects a batch of experiences with each policy. Then, it uses this batch to update the policy. This cycle repeats until...