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Python Reinforcement Learning Projects

You're reading from Python Reinforcement Learning Projects

Product type Book

Published in Sep 2018

Publisher Packt

ISBN-13 9781788991612

Pages 296 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Authors (3):

Sean Saito

Yang Wenzhuo

Rajalingappaa Shanmugamani

View More author details

Table of Contents (17) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Up and Running with Reinforcement Learning

2. Balancing CartPole

3. Playing Atari Games

4. Simulating Control Tasks

5. Building Virtual Worlds in Minecraft

6. Learning to Play Go

7. Creating a Chatbot

8. Generating a Deep Learning Image Classifier

9. Predicting Future Stock Prices

10. Looking Ahead

1. Other Books You May Enjoy

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Index

Data preparation

In the Atari environment, recall that there are three modes for each Atari game, for example, Breakout, BreakoutDeterministic, and BreakoutNoFrameskip, and each mode has two versions, for example, Breakout-v0 and Breakout-v4. The main difference between the three modes is the frameskip parameter that indicates the number of frames (steps) the one action is repeated on. This is called the frame-skipping technique, which allows us to play more games without significantly increasing the runtime.

However, in the Minecraft environment, there is only one mode where the frameskip parameter is equal to one. Therefore, in order to apply the frame-skipping technique, we need to explicitly repeat a certain action frameskip multiple times during one timestep. Besides this, the frame images returned by the step function are RGB images. Similar to the Atari environment, the observed frame images are converted to grayscale and then resized to 84x84. The following code provides the wrapper...

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Authors (3)

Sean Saito

Sean Saito is the youngest ever Machine Learning Developer at SAP and the first bachelor hired for the position. He currently researches and develops machine learning algorithms that automate financial processes. He graduated from Yale-NUS College in 2017 with a Bachelor of Science degree (with Honours), where he explored unsupervised feature extraction for his thesis. Having a profound interest in hackathons, Sean represented Singapore during Data Science Game 2016, the largest student data science competition. Before attending university in Singapore, Sean grew up in Tokyo, Los Angeles, and Boston.

See other products by Sean Saito

Yang Wenzhuo

Yang Wenzhuo works as a Data Scientist at SAP, Singapore. He got a bachelor's degree in computer science from Zhejiang University in 2011 and a Ph.D. in machine learning from the National University of Singapore in 2016. His research focuses on optimization in machine learning and deep reinforcement learning. He has published papers on top machine learning/computer vision conferences including ICML and CVPR, and operations research journals including Mathematical Programming.

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Rajalingappaa Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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