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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Implementing the Space Invaders gaming bot


In this section, we will be coding the Space Invaders game using DQN and Q-learning. For coding, we will be using the gym, TensorFlow, and virtualenv libraries. You can refer to the entire code by using this GitHub link: https://github.com/jalajthanaki/SpaceInvaders_gamingbot.

We are using a convolutional neural network (CNN). Here, we have defined the CNN in a separate file. The name of this file is convnet.py. Take a look at the following screenshot: at the following figure:

Figure 11.16: Code snippet for Convnrt.py

You can refer to the code using this GitHub link: https://github.com/jalajthanaki/SpaceInvaders_gamingbot/blob/master/convnet.py.

We are defining the DQN algorithm in the dqn.py script. You can refer to the code snippet shown in the following screenshot:

Figure 11.17: Code snippet for dqn.py

For training, we have defined our training logic in train.py. You can refer to the code snippet shown in the following screenshot:

Figure 11.18: Code...

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