Reader small image

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook
Published inJan 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781838826994
Edition2nd Edition
Languages
Right arrow
Author (1)
Maxim Lapan
Maxim Lapan
author image
Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

Right arrow

Summary

Despite its simplicity, and the toy-like example in this chapter, seq2seq is a very widely used model in NLP and other domains, so the alternative RL approach could potentially be applicable to a wide range of problems. In this chapter, we've just scratched the surface of deep NLP models and ideas, which go well beyond the scope of this book. We covered the basics of NLP models, such as RNNs and the seq2seq model, along with different ways that it could be trained.

In the next chapter, we will take a look at another example of the application of RL methods in another domain: automating web navigation tasks.

lock icon
The rest of the page is locked
Previous PageNext Chapter
You have been reading a chapter from
Deep Reinforcement Learning Hands-On. - Second Edition
Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

author image
Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan