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Python Reinforcement Learning Projects

You're reading from   Python Reinforcement Learning Projects Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781788991612
Length 296 pages
Edition 1st Edition
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Authors (3):
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Sean Saito Sean Saito
Author Profile Icon Sean Saito
Sean Saito
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Yang Wenzhuo Yang Wenzhuo
Author Profile Icon Yang Wenzhuo
Yang Wenzhuo
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Toc

Playing Atari Games

an a machine learn how to play video games by itself and beat human players? Solving this problem is the first step toward general artificial intelligence (AI) in the field of gaming. The key technique to creating an AI player is deep reinforcement learning. In 2015, Google's DeepMind, one of the foremost AI/machine learning research teams (who are famous for building AlphaGo, the machine that beat Go champion Lee Sedol) proposed the deep Q-learning algorithm to build an AI player that can learn to play Atari 2600 games, and surpass a human expert on several games. This work made a great impact on AI research, showing the possibility of building general AI systems.

In this chapter, we will introduce how to use gym to play Atari 2600 games, and then explain why the deep Q-learning algorithm works and how to implement it using TensorFlow. The goal is to...

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