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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 Q-Network

Deep Q-Networks (DQNs) revolutionized the field of reinforcement learning (RL). I am sure you have heard of Google DeepMind, which used to be a British company called DeepMind Technologies until Google acquired it in 2014. DeepMind published a paper in 2013 titled Playing Atari with Deep RL, where they used Deep Neural Networks (DNNs) in the context of RL, or DQNs as they are referred to – which is an idea that is seminal to the field. This paper revolutionized the field of deep RL, and the rest is history! Later, in 2015, they published a second paper, titled Human Level Control Through Deep RL, in Nature, where they had more interesting ideas that further improved the former paper. Together, the two papers led to a Cambrian explosion in the field of deep RL, with several new algorithms that have improved the training of agents using neural networks, and...

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