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

REINFORCE issues

In the previous section, we discussed the REINFORCE method, which is a natural extension of the cross-entropy method. Unfortunately, both REINFORCE and the cross-entropy method still suffer from several problems, which make both of them limited to simple environments.

Full episodes are required

First of all, we still need to wait for the full episode to complete before we can start training. Even worse, both REINFORCE and the cross-entropy method behave better with more episodes used for training (just from the fact that more episodes mean more training data, which means more accurate policy gradients). This situation is fine for short episodes in the CartPole, when in the beginning, we can barely handle the bar for more than 10 steps; but in Pong, it is completely different: every episode can last for hundreds or even thousands of frames. It's equally bad from the training perspective, as our training batch becomes very large, and from the sample efficiency...