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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 FREE CHAPTER

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

11. Other Books You May Enjoy

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The background problem

Many chatbots are created with regular machine learning natural language processing algorithms, and these focus on immediate responses. A new concept is to create chatbots with the use of deep reinforcement learning. This would mean that the future implications of our immediate responses would be considered to maintain coherence.

In this chapter, you will learn how to apply deep reinforcement learning to natural language processing. Our reward function will be a future-looking function, and you will learn how to think probabilistically through the creation of this function.

Dataset

The dataset that we will use mainly consists of conversations from selected movies. This dataset will help to stimulate and understand conversational methods in the chatbot. Also, there are movie lines, which are essentially the same as the movie conversations, albeit shorter exchanges between people. Other data sets that will be used include some containing movie titles, movie characters,...

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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.

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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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