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TensorFlow Reinforcement Learning Quick Start Guide

You're reading from   TensorFlow Reinforcement Learning Quick Start Guide Get up and running with training and deploying intelligent, self-learning agents using Python

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789533583
Length 184 pages
Edition 1st Edition
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Author (1):
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Kaushik Balakrishnan Kaushik Balakrishnan
Author Profile Icon Kaushik Balakrishnan
Kaushik Balakrishnan
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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.
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Table of Contents (11) Chapters Close

Preface 1. Up and Running with Reinforcement Learning 2. Temporal Difference, SARSA, and Q-Learning FREE CHAPTER 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 does DDQN perform better than DQN?
  2. How does the dueling network architecture help in the training?
  3. Why does prioritized experience replay speed up the training?
  4. How do sticky actions help in the training?
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Authors (1)

Profile icon Balakrishnan
Balakrishnan
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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.
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See other products by Balakrishnan

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