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)
26
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27
Index

Chatbot example

In the beginning of this chapter, we talked a bit about chatbots and NLP, so let's try to implement something simple using seq2seq and RL training. There are two large groups of chatbots: entertainment human-mimicking and goal-oriented chatbots. The first group is supposed to entertain a user by giving human-like replies to their phrases, without fully understanding them. The latter category is much harder to implement and is supposed to solve a user's problem, such as providing information, changing reservations, or switching on and off your home toaster.

Most of the latest efforts in the industry are focused on the goal-oriented group, but the problem is far from being fully solved yet. As this chapter is supposed to give a short example of the methods described, we will focus on training an entertainment bot using an online dataset with phrases extracted from movies.

Despite the simplicity of this problem, the example is large in terms of its code...