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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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Temporal difference learning

TD learning algorithms are based on reducing the differences between estimates made by the agent at different times. Q-learning, which we will discuss in the following section, is a TD algorithm, but it is based on the difference between states in immediately adjacent instants. TD is more generic and may consider moments and states further away.

TD is a combination of the ideas of the MC method and DP, both of which can be summarized as follows:

MC methods allow the solving of reinforcement learning problems based on the average of the obtained results
DP represents a set of algorithms that can be used to calculate an optimal policy given a perfect model of the environment in the form of a Markov Decision Process (MDP)

The following can be said of TD methods:

They inherit from MC methods the idea of learning directly from experience accumulated...

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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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