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Machine Learning Using TensorFlow Cookbook

You're reading from   Machine Learning Using TensorFlow Cookbook Create powerful machine learning algorithms with TensorFlow

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800208865
Length 416 pages
Edition 1st Edition
Languages
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Authors (3):
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Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Alexia Audevart Alexia Audevart
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Alexia Audevart
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Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started with TensorFlow 2.x 2. The TensorFlow Way FREE CHAPTER 3. Keras 4. Linear Regression 5. Boosted Trees 6. Neural Networks 7. Predicting with Tabular Data 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Transformers 11. Reinforcement Learning with TensorFlow and TF-Agents 12. Taking TensorFlow to Production 13. Other Books You May Enjoy
14. Index

GridWorld

The code in this section is adapted from https://github.com/sachag678.

We begin by demonstrating the basic TF-Agents functionality in the GridWorld environment. RL problems are best studied in the context of either games (where we have a clearly defined set of rules and fully observable context), or toy problems such as GridWorld. Once the basic concepts are clearly defined in a simplified but non-straightforward environment, we can move to progressively more challenging situations.

The first step is to define a GridWorld environment: this is a 6x6 square board, where the agent starts at (0,0), the finish is at (5,5), and the goal of the agent is to find the path from the start to the finish. Possible actions are moves up/down/left/right. If the agent lands on the finish, it receives a reward of 100, and the game terminates after 100 steps if the end was not reached by the agent. An example of the GridWorld "map" is provided here:

Figure...

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