You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Length 826 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Chatbots

Author (1):

Maxim Lapan

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Table of Contents (28) Chapters

Preface

1. What Is Reinforcement Learning?

2. OpenAI Gym FREE CHAPTER

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. Higher-Level RL Libraries

8. DQN Extensions

9. Ways to Speed up RL

10. Stocks Trading Using RL

11. Policy Gradients – an Alternative

12. The Actor-Critic Method

13. Asynchronous Advantage Actor-Critic

14. Training Chatbots with RL

15. The TextWorld Environment

16. Web Navigation

17. Continuous Action Space

18. RL in Robotics

19. Trust Regions – PPO, TRPO, ACKTR, and SAC

20. Black-Box Optimization in RL

21. Advanced Exploration

22. Beyond Model-Free – Imagination

23. AlphaGo Zero

24. RL in Discrete Optimization

25. Multi-agent RL

26. Other Books You May Enjoy

27. Index

Higher-Level RL Libraries

In Chapter 6, Deep Q-Networks, we implemented the deep Q-network (DQN) model published by DeepMind in 2015 (https://deepmind.com/research/publications/playing-atari-deep-reinforcement-learning). This paper had a significant effect on the RL field by demonstrating that, despite common belief, it's possible to use nonlinear approximators in RL. This proof of concept stimulated great interest in the deep Q-learning field and in deep RL in general.

In this chapter, we will take another step towards practical RL by discussing higher-level RL libraries, which will allow you to build your code from higher-level blocks and focus on the details of the method that you are implementing. Most of the chapter will describe the PyTorch Agent Net (PTAN) library, which will be used in the rest of the book to avoid code repetition, so will be covered in detail.

We will cover: