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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? FREE CHAPTER

2. OpenAI Gym

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

Chapter 12. Chatbots Training with RL

In this chapter, we'll take a look at another practical application of Deep Reinforcement Learning (Deep RL), which has become popular over the Past two years: the training of natural language models with RL methods. It started with a paper called Recurrent Models of Visual Attention, published in 2014, and has been successfully applied to a wide variety of problems from the Natural Language Processing (NLP) domain.

To understand the method, we will begin with a brief introduction to the NLP basics, including Recurrent Neural Networks (RNNs), word embeddings, and the seq2seq model. Then we'll discuss similarities between the NLP and RL problems and take a look at original ideas on how to improve NLP seq2seq training using RL methods. The core of the chapter is a dialogue system trained on the movie dialogues dataset.

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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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