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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
Languages
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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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Table of Contents (11) Chapters Close

Preface 1. Up and Running with Reinforcement Learning FREE CHAPTER 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

Deep RL Applied to Autonomous Driving

Autonomous driving is one of the hottest technological revolutions in development as of the time of writing this. It will dramatically alter how humanity looks at transportation in general, and will drastically reduce travel costs as well as increase safety. Several state-of-the-art algorithms are used by the autonomous vehicle development community to this end. These include, but are not limited to, perception, localization, path planning, and control. Perception deals with the identification of the environment around an autonomous vehicle—pedestrians, cars, bicycles, and so on. Localization involves the identification of the exact location—or pose to be more precise—of the vehicle in a precomputed map of the environment. Path planning, as the name implies, is the process of planning the path of the autonomous vehicle...

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