Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Python Deep Learning

You're reading from   Python Deep Learning Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Arrow left icon
Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789348460
Length 386 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (5):
Arrow left icon
Gianmario Spacagna Gianmario Spacagna
Author Profile Icon Gianmario Spacagna
Gianmario Spacagna
Daniel Slater Daniel Slater
Author Profile Icon Daniel Slater
Daniel Slater
Valentino Zocca Valentino Zocca
Author Profile Icon Valentino Zocca
Valentino Zocca
Peter Roelants Peter Roelants
Author Profile Icon Peter Roelants
Peter Roelants
Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
+1 more Show less
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Machine Learning - an Introduction FREE CHAPTER 2. Neural Networks 3. Deep Learning Fundamentals 4. Computer Vision with Convolutional Networks 5. Advanced Computer Vision 6. Generating Images with GANs and VAEs 7. Recurrent Neural Networks and Language Models 8. Reinforcement Learning Theory 9. Deep Reinforcement Learning for Games 10. Deep Learning in Autonomous Vehicles 11. Other Books You May Enjoy

Driving policy with ChauffeurNet

In this section, we'll discuss a recent paper called ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst (https://arxiv.org/abs/1812.03079). It was released in December 2018 by Waymo, one of the leaders in the AV space. The following are some of the properties of the ChauffeurNet model:

  • It is a combination of two interconnected networks. The first is a CNN called FeatureNet, which extracts features from the environment. These features are fed as inputs to a second, recurrent network called AgentRNN, which them to determine the driving policy.
  • It uses imitation supervised learning similarly to the algorithms we described in the Imitation driving policy section. The training set is generated based on records of real-world driving episodes. ChauffeurNet can handle complex driving situations such as lane changes,...
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
Banner background image