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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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Questions

What is the RL equivalent of a labeled training dataset in supervised learning?
What is one of the difficulties of not having a standardized set of environments for developing RL algorithms? How does Gym attempt to solve this problem?
What is the difference between an actuated joint and an unactuated joint?
What is the benefit of being able to use a single algorithm to solve more than one environment? Explain in two to three sentences.
What is the importance of being able to solve generalized control problems in robotics motion?
Briefly describe the relationship between a probability distribution and our current estimation of the likelihood of an event.

Explain what difference it makes to have a state space available in a contextual bandit problem.
Describe the differences in the results of A/B testing versus multi-armed bandit testing in two to three sentences.

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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.

See other products by Nazia Habib

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