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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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The learning parameters – alpha, gamma, and epsilon

Here's an updated version of the Bellman equation:

Compare it to the version we used in the last section:

In the new version, we've added in an alpha term, which means we need to include the current Q-value of the state-action pair and discount it by the alpha value.

The first equation is telling us that the new Q-value (the right side of the equation) of our state-action pair is equal to the old Q-value plus the current reward and the discounted future reward, minus the old Q-value multiplied by the alpha term. Because the alpha value is relatively small, more of the current Q-value is incorporated into the new Q-value. In both versions of the equation, because the gamma value is also relatively small, current rewards are valued more highly than future rewards.

Notice that, if the alpha value is 1, the first...

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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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