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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Index

An overview of chatbots

A trending topic in recent years has been AI-driven chatbots. There are various opinions on the subject, ranging from chatbots being completely useless to being an absolutely brilliant idea, but one thing is hard to question: chatbots open up new ways for people to communicate with computers that are much more human-like and natural than the old-style interfaces that we are all used to.

At its core, a chatbot is a computer program that uses natural language to communicate with other parties (humans or other computer programs) in a form of dialogue.

Such scenarios can take many different forms, such as one chatbot talking to a user, many chatbots talking to each other, and so on. For example, there might be a technical support chatbot that can answer free-text questions from users. However, usually chatbots share common properties of a dialogue interaction (the user asks a question, but the chatbot can ask clarifying questions to get the missing information...