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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

Leave a review - let other readers know what you think

Running a Rainbow network on Dopamine

In 2018, some engineers at Google released an open source, lightweight, TensorFlow-based framework for training RL agents, called Dopamine. Dopamine, as you may already know, is the name of an organic chemical that plays an important role in the brain. We will use Dopamine to run Rainbow.

The Dopamine framework is based on four design principles:

Easy experimentation
Flexible development
Compact and reliable
Reproducible

To download Dopamine from GitHub, type the following command in a Terminal:

git clone https://github.com/google/dopamine.git

We can test whether Dopamine was successfully installed by typing the following commands into a Terminal:

cd dopamine
export PYTHONPATH=${PYTHONPATH}:.
python tests/atari_init_test.py

The output of this will look something like the following:

2018-10-27 23:08:17.810679: I tensorflow/core/platform/cpu_feature_guard...

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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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