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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

Experiments

The full implementation of the deep Q-learning algorithm can be downloaded from GitHub (link xxx). To train our AI player for Breakout, run the following command under the src folder:

python train.py -g Breakout -d gpu

There are two arguments in train.py. One is -g or --game, indicating the name of the game one wants to test. The other one is -d or --device, which specifies the device (CPU or GPU) one wants to use to train the Q-network.

For Atari games, even with a high-end GPU, it will take 4-7 days to make our AI player achieve human-level performance. In order to test the algorithm quickly, a special game called demo is implemented as a lightweight benchmark. Run the demo via the following:

python train.py -g demo -d cpu

The demo game is based on the GridWorld game on the website at https://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html:

In this game, a robot in a 2D grid world has nine eyes pointing in different angles, and each eye senses three values along its direction...

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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.

See other products by Yang Wenzhuo

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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