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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Deep Q-networks

DQN is a seminal work by (Mnih et al., 2015) that made deep RL a viable approach to complex sequential control problems. The authors demonstrated that a single DQN architecture can achieve super-human level performance in many Atari games without any feature engineering, which created a lot of excitement regarding the progress of AI. Let's look into what makes DQN so effective compared to the algorithms we mentioned earlier.

Key concepts in deep Q-networks

DQN modifies online Q-learning with two important concepts by using experience replay and a target network, which greatly stabilize the learning. We describe these concepts next.

Experience replay

As mentioned earlier, simply using the experience sampled sequentially from the environment leads to highly correlated gradient steps. DQN, on the other hand, stores those experience tuples, , in a replay buffer (memory), an idea that was introduced back in 1993 (Lin, 1993). During learning, the samples...