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Advanced Deep Learning with Keras

You're reading from   Advanced Deep Learning with Keras Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788629416
Length 368 pages
Edition 1st Edition
Languages
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (13) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras FREE CHAPTER 2. Deep Neural Networks 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods Other Books You May Enjoy Index

Q-Learning on OpenAI gym

Before presenting another example, there appears to be a need for a suitable RL simulation environment. Otherwise, we can only run RL simulations on very simple problems like in the previous example. Fortunately, OpenAI created Gym, https://gym.openai.com.

The gym is a toolkit for developing and comparing RL algorithms. It works with most deep learning libraries, including Keras. The gym can be installed by running the following command:

$ sudo pip3 install gym

The gym has several environments where an RL algorithm can be tested against such as toy text, classic control, algorithmic, Atari, and 2D/3D robots. For example, FrozenLake-v0 (Figure 9.5.1) is a toy text environment similar to the simple deterministic world used in the Q-Learning in Python example. FrozenLake-v0 has 12 states. The state marked S is the starting state, F is the frozen part of the lake which is safe, H is the Hole state that should be avoided, and G is the Goal state where the frisbee...

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