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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
2. Brushing Up on Reinforcement Learning Concepts FREE CHAPTER 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

Getting started with OpenAI Gym

In this section, we'll get familiar with the OpenAI Gym package and learn how to get it up and running in your Python development environment.

What is Gym?

OpenAI Gym is a toolkit of environments for building RL algorithms. In the context of RL, an environment refers to the actual task (a Markov decision process or MDP) that is to be solved by the algorithm. The state and action spaces of the task are defined by the environment.

Be careful not to get the RL use of the word environment confused with others that we might use in different contexts, such as a Python development environment or a Unix/Linux environment. Those terms refer to very different things.

The following diagram shows...

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