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Hands-On Q-Learning with Python

You're reading from Hands-On Q-Learning with Python

Product type Book

Published in Apr 2019

Publisher Packt

ISBN-13 9781789345803

Pages 212 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Nazia Habib

Table of Contents (14) Chapters

Preface

1. Section 1: Q-Learning: A Roadmap

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

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Thompson sampling and the Bayesian control rule

We'll go over the concepts of Thompson sampling and Bayesian control problems at a high level, but they are well worth exploring further as an extension of the topics covered in this book.

Thompson sampling

Essentially, Thompson sampling has us believing what the prior probability distribution is and updating it every time we get new information about the environment. Eventually, our updated belief will coincide with the true probability distribution. This approach is fundamentally Bayesian. This is because it treats the probability distribution as our current lack of knowledge about the environment and updates it according to the new information we get.

In Thompson sampling...

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Authors (1)

Nazia Habib

Nazia Habib is a data scientist who has worked in a variety of industries to generate predictive analytics solutions for diverse groups of stakeholders. She is an expert in building solutions to optimization problems under conditions of uncertainty. Her projects range from predicting user behavior and engagement with social media apps to designing adaptive testing software. Her ongoing specialization is in designing custom reinforcement learning algorithms for modeling control problems with limited inputs that converge to optimal solutions.

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