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

Things to try

If you are curious and want to experiment with this chapter's material on your own, then here is a short list of directions to explore. Be warned though: they can take lots of time and may cause you some moments of frustration during your experiments. However, these experiments are a very efficient way to really master the material from a practical point of view:

Try to take some other games from the Atari set, such as Breakout, Atlantis, or River Raid (my childhood favorite). This could require the tuning of hyperparameters.
As an alternative to FrozenLake, there is another tabular environment, Taxi, which emulates a taxi driver who needs to pick up passengers and take them to a destination.
Play with Pong hyperparameters. Is it possible to train faster? OpenAI claims that it can solve Pong in 30 minutes using the asynchronous advantage actor-critic method (which is a subject of part three of this book). Maybe it's possible with a DQN.
Can...