Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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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.