Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning Solutions

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

Arrow left icon
Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
Arrow right icon
View More author details
Toc

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

Building the Pong gaming bot


In this section, we will be looking at how we can build a gaming bot that can learn the game of Pong. Before we start, we will look at the approach and concepts that we will be using for building the Pong gaming bot.

Understanding the key concepts

In this section, we will be covering some aspects of building the Pong game bot, which are as follows:

  • Architecture of the gaming bot

  • Approach for the gaming bot

Architecture of the gaming bot

In order to develop the Pong gaming bot, we are choosing a neural-network-based approach. The architecture of our neural network is crucial. Let's look at the architectural components step by step:

  1. We take the gaming screen as the input and preprocess it as per the DQN algorithm.

  2. We pass this preprocessed screen to an neural network (NN.)

  3. We use a gradient descent to update the weights of the NN.

  4. Weight [1]: This matrix holds the weights of pixels passing into the hidden layer. The dimension will be [200 x 80 x 80] – [200 x 6400].

  5. Weight...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at AU $24.99/month. Cancel anytime