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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

Deep Q-learning

Here comes the fun part—the brain design of our AI Atari player. The core algorithm is based on deep reinforcement learning or deep RL. In order to understand it better, some basic mathematical formulations are required. Deep RL is a perfect combination of deep learning and traditional reinforcement learning. Without understanding the basic concepts about reinforcement learning, it is difficult to apply deep RL correctly in real applications, for example, it is possible that someone may try to use deep RL without defining state space, reward, and transition properly.

Well, don't be afraid of the difficulty of the formulations. We only need high school-level mathematics, and will not go deep into the mathematical proofs of why traditional reinforcement learning algorithms work. The goal of this chapter is to learn the basic Q-learning algorithm, to know...

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