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Python Deep Learning Cookbook

You're reading from   Python Deep Learning Cookbook Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787125193
Length 330 pages
Edition 1st Edition
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Author (1):
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Indra den Bakker Indra den Bakker
Author Profile Icon Indra den Bakker
Indra den Bakker
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Toc

Table of Contents (15) Chapters Close

Preface 1. Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks 2. Feed-Forward Neural Networks FREE CHAPTER 3. Convolutional Neural Networks 4. Recurrent Neural Networks 5. Reinforcement Learning 6. Generative Adversarial Networks 7. Computer Vision 8. Natural Language Processing 9. Speech Recognition and Video Analysis 10. Time Series and Structured Data 11. Game Playing Agents and Robotics 12. Hyperparameter Selection, Tuning, and Neural Network Learning 13. Network Internals 14. Pretrained Models

Learning to play games with deep reinforcement learning

In this recipe, we will demonstrate how to leverage deep learning models to play games. In our example, we show how to apply a Deep-Q Network for playing breakout with the Keras framework.

How to do it...

  1. Let's start with importing the necessary libraries, as follows:
import gym
import random
import numpy as np
import matplotlib.pyplot as plt
from collections import deque

from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import Dense, Flatten
from keras.layers.convolutional import Conv2D
from keras import backend as K
  1. First, we will plot an example input image of the game:
Figure 11.1: Example input image of Breakout by OpenAI
env...
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