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

Summary

In this chapter, we just touched a bit on the very interesting and dynamic field of MARL. There are lots of things that you can try on your own using the MAgent environment or other environments (like PySC2).

My congratulations on reaching the end of the book! I hope that the book was useful and you enjoyed reading it as much as I enjoyed gathering the material and writing all the chapters. As a final word, I would like to wish you good luck in this exciting and dynamic area of RL. The domain is developing very rapidly, but with an understanding of the basics, it will become much simpler for you to keep track of the new developments and research in this field.

There are many very interesting topics left uncovered, such as partially observable Markov decision processes (where environment observations don't fulfill the Markov property) or recent approaches to exploration, such as the count-based methods. There has been a lot of recent activity around multi-agent methods...