Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Deep Reinforcement Learning Hands-On
  • Table Of Contents Toc
  • Feedback & Rating feedback
Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
4.3 (38)
close
close
Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On

4.3 (38)
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)
close
close
26
Other Books You May Enjoy
27
Index

RL in Robotics

This chapter is a bit unusual in comparison to the other chapters in this book for the following reasons:

  • It took me almost four months to gather all the materials, do the experiments, write the examples, and so on
  • This is the only chapter in which we will try to step beyond emulated environments into the physical world
  • In this chapter, we will build a small robot from accessible and cheap components to be controlled using reinforcement learning (RL) methods

This topic is an amazing and fascinating field for many reasons that couldn't be covered in a whole book, much less in a short chapter. So, this chapter doesn't pretend to offer anywhere close to complete coverage of the robotics field. It is just a short introduction that shows what can be done with commodity components, and outlines future directions for your own experiments and research. In addition, I have to admit that I'm not an expert in robotics and have never worked...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY