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

Seq2seq training

That's all very interesting, but how is it related to RL? The connection lies in the training process of the seq2seq model, but before we come to the modern RL approaches to the problem, I need to say a couple of words about the standard way of carrying out the training.

Log-likelihood training

Imagine that we need to create a machine translation system from one language (say, French) into another language (English) using the seq2seq model. Let's assume that we have a good, large dataset of sample translations with French-English sentences that we're going to train our model on. How do we do this?

The encoding part is obvious: we just apply our encoder RNN to the first sentence in the training pair, which produces an encoded representation of the sentence. The obvious candidate for this representation is the hidden state returned from the last RNN application. At the encoding stage, we ignore the RNN's outputs, taking into account only...