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Keras Reinforcement Learning Projects

You're reading from Keras Reinforcement Learning Projects

Product type Book

Published in Sep 2018

Publisher Packt

ISBN-13 9781789342093

Pages 288 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Author (1):

Giuseppe Ciaburro

Table of Contents (13) Chapters

Preface

1. Overview of Keras Reinforcement Learning

2. Simulating Random Walks

3. Optimal Portfolio Selection

4. Forecasting Stock Market Prices

5. Delivery Vehicle Routing Application

6. Continuous Balancing of a Rotating Mechanical System

7. Dynamic Modeling of a Segway as an Inverted Pendulum System

8. Robot Control System Using Deep Reinforcement Learning

9. Handwritten Digit Recognizer

10. Playing the Board Game Go

11. What's Next?

12. Other Books You May Enjoy

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Summary

In this chapter, we explored some practical examples of the application of technologies based on reinforcement learning.

First, we addressed the DeepMind AlphaZero project. AlphaZero is an artificial-intelligence algorithm based on machine learning techniques developed by Google DeepMind. It is a generalization of AlphaGo Zero, the predecessor developed specifically for the game of Go and in turn the evolution of AlphaGo, the first software capable of achieving superhuman performances in the game of Go.

Then, we explored the IBM Watson project. This is a system of artificial intelligence, able to answer questions expressed in natural language, developed within the DeepQA project of IBM by the research team directed by David Ferrucci. We looked at Unity's ML-Agents toolkit, FANUC industrial robots, and an automated trading system.

Finally, we discussed some future...

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Authors (1)

Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

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