Applied Reinforcement Learning with Python: With OpenAI Gym, Tensorflow, and Keras

by Taweh Beysolow II

Overview

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.

Product Details

ISBN-13:	9781484251263
Publisher:	Apress
Publication date:	08/24/2019
Edition description:	First Edition
Pages:	168
Product dimensions:	5.90(w) x 9.00(h) x 0.50(d)

About the Author

Taweh Beysolow II is a data scientist and author currently based in the United States. He has a Bachelor of Science degree in economics from St. Johns University and a Master of Science in Applied Statistics from Fordham University. After successfully exiting the startup he co-founded, he now is a Director at Industry Capital, a San Francisco based Private Equity firm, where he helps lead the Crypurrency and Blockchain platforms.

Table of Contents

Chapter 1: Introduction to Reinforcement Learning.- Chapter 2: Reinforcement Learning Algorithms.- Chapter 3: Q Learning.- Chapter 4: Reinforcement Learning Based Market Making.- Chapter 5: Reinforcement Learning for Video Games.