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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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Chapter 6, Digging Deeper into Deep Q-Networks with Keras and TensorFlow

Keras abstracts many of the functionalities provided by TensorFlow and creates a high-level frontend for creating complex deep learning architectures.
CartPole is effectively a binary prediction problem because there are two options provided for every action taken.
When the state space is very large, some states can be grouped together and treated similarly when the optimal actions to take from those states are the same.

Experience Replay updates the Q-function using samples of past actions rather than updating it after every action. This helps prevent overfitting by smoothing away outlier actions and having the agent forget previous experiences in favor of new ones.
An RL model approximating Q-values does not know what those actual Q-values are and progressively develops estimates for those values. A...

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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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