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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

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781789345803

Length 212 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Author (1):

Nazia Habib

View More author details

Table of Contents (14) Chapters

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

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Questions

What is the difference between using Keras as a wrapper for TensorFlow and using TensorFlow by itself?
What kind of prediction problem is CartPole? What type of target is being predicted?
What advantage does binning the state space provide?
Give a 2–3 sentence explaining how experience replay works.
Explain the moving target problem in estimating Q-values.
Explain what maximization bias is.
When does maximization bias tend to happen?
What advantage do dueling DQNs provide over regular DQNs?

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Authors (1)

Nazia Habib

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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