#### Overview of this book

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.
Preface
Free Chapter
Overview of Keras Reinforcement Learning
Simulating Random Walks
Optimal Portfolio Selection
Forecasting Stock Market Prices
Delivery Vehicle Routing Application
Continuous Balancing of a Rotating Mechanical System
Dynamic Modeling of a Segway as an Inverted Pendulum System
Robot Control System Using Deep Reinforcement Learning
Handwritten Digit Recognizer
Playing the Board Game Go
What's Next?
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# 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...