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

Chatbot example

In the beginning of this chapter, we talked a bit about chatbots and NLP, so let's try to implement something simple using seq2seq and RL training. There are two large groups of chatbots: entertainment human-mimicking and goal-oriented chatbots. The first group is supposed to entertain a user by giving human-like replies to their phrases, without fully understanding them. The latter category is much harder to implement and is supposed to solve a user's problem, such as providing information, changing reservations, or switching on and off your home toaster.

Most of the latest efforts in the industry are focused on the goal-oriented group, but the problem is far from being fully solved yet. As this chapter is supposed to give a short example of the methods described, we will focus on training an entertainment bot using an online dataset with phrases extracted from movies.

Despite the simplicity of this problem, the example is large in terms of its code...