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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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Your Q-learning agent in its environment

Let's talk about the self-driving taxi agent that we'll be building. Recall that the Taxi-v2 environment has 500 states, and 6 possible actions that can be taken from each state.

Your objective in the taxi environment is to pick up a passenger at one location, and drop them off at their desired destination in as few timesteps as possible.

You receive points for a successful drop-off, and lose points for the time it takes to complete the task, so your goal is to complete the task in as little time as possible. You also lose points for incorrect actions, such as dropping a passenger off at the wrong location.

Because your goal is to get to both the pickup and drop-off locations as quickly as possible, you lose one point for every move you make per timestep.

Your agent's goal in solving this problem is to find the optimal policy...

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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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