Overview
- Understand how to package and deploy solutions in Python that utilize deep learning
- Includes specific topics such as Q learning and deep reinforcement-learning
- Covers the latest reinforcement learning packages
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Table of contents (5 chapters)
Keywords
About this book
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym.
Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.
What You'll Learn
- Implement reinforcement learning with Python
- Work with AI frameworks such as OpenAI Gym, Tensorflow, and Keras
- Deploy and train reinforcement learning–based solutions via cloud resources
- Apply practical applications of reinforcement learning
Who This Book Is For
Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Applied Reinforcement Learning with Python
Book Subtitle: With OpenAI Gym, Tensorflow, and Keras
Authors: Taweh Beysolow II
DOI: https://doi.org/10.1007/978-1-4842-5127-0
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Taweh Beysolow II 2019
Softcover ISBN: 978-1-4842-5126-3Published: 24 August 2019
eBook ISBN: 978-1-4842-5127-0Published: 23 August 2019
Edition Number: 1
Number of Pages: XV, 168
Number of Illustrations: 47 b/w illustrations
Topics: Artificial Intelligence, Python, Open Source