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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

MABP – a classic exploration versus exploitation problem

Several MABP environments have been created for OpenAI Gym, and they are well worth exploring for a clearer picture of how the problem works. We will not be solving a bandit problem from scratch with the code in this book, but we will go into some solutions in detail and discuss their relevance to epsilon decay strategies.

The main thing to bear in mind when solving any bandit problem is that we are always trying to discover the optimal outcome in a system by balancing our need to both explore and exploit our knowledge of our environment. Effectively, we are learning as we go and we are taking advantage of the knowledge that we already have in the process of gaining new knowledge.

Setting up a bandit problem

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