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

View More author details

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

SAC

In the final section, we will check our environments on the latest state-of-the-art method, called SAC, which was proposed by a group of Berkeley researchers and introduced in the paper Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning, by Tuomas Taarnoja et. al. arXiv 1801.01290, published in 2018.

At the moment, it's considered to be one of the best methods for continuous control problems. The core idea of the method is closer to the DDPG method than to A2C policy gradients. The SAC method might have been more logically described in Chapter 17, Continuous Action Space. However, in this chapter, we have the chance to compare it directly with PPO's performance, which was considered to be the de facto standard in continuous control problems for a long time.

The central idea in the SAC method is entropy regularization, which adds a bonus reward at each timestamp that is proportional to the entropy of the policy at this timestamp. In mathematical...