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

Robots and robotics

I'm sure you know what the word "robot" means and have seen them both in real life and in science fiction movies. Putting aside fictitious ones, there are many robots in industry (check "Tesla assembly line" on YouTube), the military (if you haven't seen the Boston Dynamics videos, you should stop reading and check them out), agriculture, medicine, and our homes. Automatic vacuum cleaners, modern coffee machines, 3D printers, and many other applications are examples of specialized mechanisms with complicated logic that are driven by some kind of software. At a high level, all those robots have common features, which we're going to discuss. Of course, this kind of classification is not perfect. As is often the case, there are lots of outliers that might fulfill the given criteria, but could still hardly be considered as robots.

Firstly, robots are connected to the world around them with some kind of sensors or other communication...