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Keras Reinforcement Learning Projects

You're reading from   Keras Reinforcement Learning Projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789342093
Length 288 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (13) Chapters Close

Preface 1. Overview of Keras Reinforcement Learning FREE CHAPTER 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

Basics of graph theory

As anticipated at the beginning of this chapter, what we will face is an optimization problem or (what is the same) we will try to identify the shortest path. Graphs are data structures that are widely used in optimization problems. A graph is graphically represented by a vertex and edge structure. The vertices can be seen as events from which different alternatives (the edge) depart. Typically, graphs are used to represent a network in an unambiguous way: vertices represent individual calculators, road intersections, or bus stops, and edges are electrical connections or roads. Edges can connect vertices in any way possible.

Graph theory is a branch of mathematics that allows you to describe sets of objects together with their relationships; it was born in 1700 with Leonhard Euler.

A graph is indicated in a compact way, with G = (V, E), where V indicates...

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