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

Thanks for reaching the end! I hope you enjoyed reading this chapter as much as I enjoyed writing it. This field is very interesting; we have just touched on it a little, but I hope that this chapter will show you a direction for your own experiments and projects. The goal of the chapter wasn't building a robot that will stand, as this could be done in a much easier and more efficient way; the true goal was to show how the RL way of thinking can be applied to robotics problems, and how you can do your own experiments with real hardware without having access to expensive robotic arms, complex robots, and so on.

At the same time, I see some potential for the RL approach to be applied to complex robots, and who knows, maybe you will build the next version of iRobot Corporation to bring more robots into our lives. If you are interested in buying the kits for the robot platform described in this chapter, it would be really helpful if you could fill out this form: https:...