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TensorFlow Reinforcement Learning Quick Start Guide

You're reading from  TensorFlow Reinforcement Learning Quick Start Guide

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789533583
Pages 184 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Kaushik Balakrishnan Kaushik Balakrishnan
Profile icon Kaushik Balakrishnan
LinkedIn
Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.
See other products by Kaushik Balakrishnan

Table of Contents (11) Chapters

Preface 1. Up and Running with Reinforcement Learning 2. Temporal Difference, SARSA, and Q-Learning 3. Deep Q-Network 4. Double DQN, Dueling Architectures, and Rainbow 5. Deep Deterministic Policy Gradient 6. Asynchronous Methods - A3C and A2C 7. Trust Region Policy Optimization and Proximal Policy Optimization 8. Deep RL Applied to Autonomous Driving 9. Assessment 10. Other Books You May Enjoy

Questions

  1. Why is a replay buffer used in a DQN?
  2. Why do we use target networks?
  3. Why do we stack four frames into one state? Will one frame alone suffice to represent one state?
  4. Why is the Huber loss sometimes preferred over L2 loss?
  5. We converted the RGB input image into grayscale. Can we instead use the RGB image as input to the network? What are the pros and cons of using RGB images instead of grayscale?
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Authors (1)

Profile icon Kaushik Balakrishnan
LinkedIn
Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.
Read more
See other products by Kaushik Balakrishnan

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