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Python Reinforcement Learning Projects

You're reading from Python Reinforcement Learning Projects Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

Product type Paperback

Published in Sep 2018

Publisher Packt

ISBN-13 9781788991612

Length 296 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Reinforcement Learning

Authors (3):

Sean Saito

Rajalingappaa Shanmugamani

Yang Wenzhuo

View More author details

Table of Contents (12) Chapters

Preface

1. Up and Running with Reinforcement Learning

2. Balancing CartPole FREE CHAPTER

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

11. Other Books You May Enjoy

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AlphaGo Zero

We will cover AlphaGo Zero, the upgraded version of its predecessor before we finally get into some coding. The main features of AlphaGo Zero address some of the drawbacks of AlphaGo, including its dependency on a large corpus of games played by human experts.

The main differences between AlphaGo Zero and AlphaGo are the following:

AlphaGo Zero is trained solely with self-play reinforcement learning, meaning it does not rely on any human-generated data or supervision that is used to train AlphaGo
Policy and value networks are represented as one network with two heads rather than two separate ones
The input to the network is the board itself as an image, such as a 2D grid; the network does not rely on heuristics and instead uses the raw board state itself
In addition to finding the best move, Monte Carlo tree search is also used for policy iteration and evaluation; moreover, AlphaGo Zero does not conduct rollouts during a search

Training AlphaGo Zero

Since we don't use human-generated...

The rest of the chapter is locked

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Authors (3)

Sean Saito

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

Shanmugamani

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.

See other products by Shanmugamani

Yang Wenzhuo

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

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