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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

Reinforcement Learning

Author (1):

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

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Understanding SARSA and Q-Learning

In this section, we will learn about SARSA and Q-Learning and how can they are coded with Python. Before we go further, let's find out what SARSA and Q-Learning are. SARSA is an algorithm that uses the state-action Q values to update. These concepts are derived from the computer science field of dynamic programming, while Q-learning is an off-policy algorithm that was first proposed by Christopher Watkins in 1989, and is a widely used RL algorithm.

Learning SARSA

SARSA is another on-policy algorithm that was very popular, particularly in the 1990s. It is an extension of TD-learning, which we saw previously, and is an on-policy algorithm. SARSA keeps an update of the state-action value...

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Authors (1)

Kaushik Balakrishnan

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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