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

Chatbot training

Natural language understanding was the stuff of science fiction for a long time. In science fiction, you can just chat with your starship's computer to get useful and relevant information about the recent alien invasion, without pressing any button. This scenario was exploited by authors and filmmakers for decades, but in real life, such interactions with computers started to become a reality only recently. You still can't talk to your starship, but you can, at least, switch your toaster on and off without pushing buttons, which is undoubtedly a major step forward!

The reason why it took computers so long to understand language is simply due to the complexity of language itself. Even in trivial scenarios—like saying, "Toaster, switch on!"—you can imagine several ways to formulate your command, and it's usually very hard to capture all those ways and corner cases in advance using normal computer programming techniques. Unfortunately...