Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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Training Chatbots with RL

In this chapter, we will take a look at another practical application of deep reinforcement learning (RL), which has become popular over the past several years: the training of natural language models with RL methods. It started with a paper called Recurrent Models of Visual Attention (, which was published in 2014, and has been successfully applied to a wide variety of problems from the natural language processing (NLP) domain.

In this chapter, we will:

  • Begin with a brief introduction to the NLP basics, including recurrent neural networks (RNNs), word embedding, and the seq2seq (sequence-to-sequence) model
  • Discuss similarities between NLP and RL problems
  • 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 a movie dialogues dataset: the Cornell Movie-Dialogs Corpus.