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

Dataset exploration

It's always a good idea to look at your dataset from various angles, like counting statistics, plotting various characteristics of data, or just eyeballing your data to get a better understanding of your problem and potential issues. The tool cor_reader.py supports the minimalistic functionality for data analysis. By running it with the --show-genres option, you will get all genres from the dataset with a number of movies in each, sorted by the count of movies in order of decreasing size. The top 10 of them are shown as follows:

$ ./cor_reader.py --show-genres
Genres:
drama: 320
thriller: 269
action: 168
comedy: 162
crime: 147
romance: 132
sci-fi: 120
adventure: 116
mystery: 102
horror: 99

The --show-dials option displays dialogues from the movies without any preprocessing, in the order they appear in the database. The number of dialogues is large, so it's worth passing the -g option to filter by genre...