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Deep Reinforcement Learning Hands-On

You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788834247

Length 546 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

View More author details

Table of Contents (21) 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. DQN Extensions

8. Stocks Trading Using RL

9. Policy Gradients – An Alternative

10. The Actor-Critic Method

11. Asynchronous Advantage Actor-Critic

12. Chatbots Training with RL

13. Web Navigation

14. Continuous Action Space

15. Trust Regions – TRPO, PPO, and ACKTR

16. Black-Box Optimization in RL

17. Beyond Model-Free – Imagination

18. AlphaGo Zero

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Index

Deep NLP basics

Hopefully, you're excited about chatbots and their potential applications, so let's now get to the boring details of NLP building blocks and standard approaches. As with almost everything in ML, deep NLP is experiencing hype and is evolving at a fast pace, so this section just scratches the surface and covers the most common and standard building blocks. For a more detailed description, Richard Socher's online course CS224d (http://cs224d.stanford.edu) is a really good starting point.

Recurrent Neural Networks

NLP has its own specificities that make it different from computer vision or other domains. One such feature is the processing of variable-length objects. At various levels, NLP is dealing with objects that could have different lengths, for example, a word in a language could contain several characters. Sentences are formed from variable-length word sequences. Paragraphs or documents consist of varying amounts of sentences. Such variability...

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Authors (1)

Maxim Lapan

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.

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