Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Arrow left icon
Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Length 826 pages
Edition 2nd Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
Arrow right icon
View More author details
Toc

Table of Contents (28) Chapters Close

Preface 1. What Is Reinforcement Learning? 2. OpenAI Gym FREE CHAPTER 3. Deep Learning with PyTorch 4. The Cross-Entropy Method 5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks 7. Higher-Level RL Libraries 8. DQN Extensions 9. Ways to Speed up RL 10. Stocks Trading Using RL 11. Policy Gradients – an Alternative 12. The Actor-Critic Method 13. Asynchronous Advantage Actor-Critic 14. Training Chatbots with RL 15. The TextWorld Environment 16. Web Navigation 17. Continuous Action Space 18. RL in Robotics 19. Trust Regions – PPO, TRPO, ACKTR, and SAC 20. Black-Box Optimization in RL 21. Advanced Exploration 22. Beyond Model-Free – Imagination 23. AlphaGo Zero 24. RL in Discrete Optimization 25. Multi-agent RL 26. Other Books You May Enjoy
27. 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime