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Hands-On Q-Learning with Python

You're reading from   Hands-On Q-Learning with Python Practical Q-learning with OpenAI Gym, Keras, and TensorFlow

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345803
Length 212 pages
Edition 1st Edition
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Author (1):
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Nazia Habib Nazia Habib
Author Profile Icon Nazia Habib
Nazia Habib
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Q-Learning: A Roadmap FREE CHAPTER
2. Brushing Up on Reinforcement Learning Concepts 3. Getting Started with the Q-Learning Algorithm 4. Setting Up Your First Environment with OpenAI Gym 5. Teaching a Smartcab to Drive Using Q-Learning 6. Section 2: Building and Optimizing Q-Learning Agents
7. Building Q-Networks with TensorFlow 8. Digging Deeper into Deep Q-Networks with Keras and TensorFlow 9. Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym
10. Decoupling Exploration and Exploitation in Multi-Armed Bandits 11. Further Q-Learning Research and Future Projects 12. Assessments 13. Other Books You May Enjoy

More OpenAI Gym environments

OpenAI Gym has modeled a number of classic control theory problems as RL environments, including the inverted pendulum problem we already worked with in CartPole.

We'll go through some of the more popular environments in the next section. Keep in mind that Gym environments have a shared interface that lets you write generalized algorithms that can potentially be used to solve more than one environment.

You can also create your own environments and upload them to Gym if you carry out the full installation. Instructions are available in the Gym documentation.

Pendulum

The pendulum environment implements the classic inverted pendulum swing-up problem in control theory:

The pendulum is of the...

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