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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Index

Why speed matters

First, let's talk a bit about why speed is important and why we optimize it at all. It might not be obvious, but enormous hardware performance improvements have happened in the last decade or two. 14 years ago, I was involved with a project that focused on building a supercomputer for computational fluid dynamics (CFD) simulations performed by an aircraft engine design company. The system consisted of 64 servers, occupied three 42-inch racks, and required dedicated cooling and power subsystems. The hardware alone (without cooling) cost almost $1M.

In 2005, this supercomputer occupied fourth place for Russian supercomputers and was the fastest system installed in the industry. Its theoretical performance was 922 GFLOPS (billion floating-point operations per second), but in comparison to the GTX 1080 Ti released 12 years later, all the capabilities of this pile of iron look tiny.

One single GTX 1080 Ti is able to perform 11,340 GFLOPS, which is 12.3 times...