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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
Author (1):
Nazia Habib Nazia Habib
Profile icon Nazia Habib
Toc

Table of Contents (14) Chapters close

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

OpenAI Gym and RL research

As we discussed in the first chapter, OpenAI Gym is an attempt to standardize reinforcement learning research and development and to compare RL models to each other for the purposes of developing baseline research frameworks.

The following screenshot is a still from the Neon Race Car environment with OpenAI Gym and Universe:

As RL researchers, we want to be able to develop benchmarks and widely-used, well-known training and testing datasets like the ones available for supervised learning, such as ImageNet for image recognition, the familiar iris dataset from the UCI Machine Learning Repository, or the MNIST handwritten digit dataset.

The RL analogue for a widely-used labeled training dataset is a standardized set of environments such as the one that Gym provides. A standardized set of environments lets us compare the work of different researchers,...

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