Hands-On Reinforcement Learning with R

By Giuseppe Ciaburro
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  1. Section 1 - Getting Started with Reinforcement Learning with R

About this book

Reinforcement learning (RL) is an integral part of machine learning (ML), and is used to train algorithms. With this book, you'll learn how to implement reinforcement learning with R, exploring practical examples such as using tabular Q-learning to control robots.

You'll begin by learning the basic RL concepts, covering the agent-environment interface, Markov Decision Processes (MDPs), and policy gradient methods. You'll then use R's libraries to develop a model based on Markov chains. You will also learn how to solve a multi-armed bandit problem using various R packages. By applying dynamic programming and Monte Carlo methods, you will also find the best policy to make predictions. As you progress, you'll use Temporal Difference (TD) learning for vehicle routing problem applications. Gradually, you'll apply the concepts you've learned to real-world problems, including fraud detection in finance, and TD learning for planning activities in the healthcare sector. You'll explore deep reinforcement learning using Keras, which uses the power of neural networks to increase RL's potential. Finally, you'll discover the scope of RL and explore the challenges in building and deploying machine learning models.

By the end of this book, you'll be well-versed with RL and have the skills you need to efficiently implement it with R.

Publication date:
December 2019
Publisher
Packt
Pages
362
ISBN
9781789616712

 

Section 1 - Getting Started with Reinforcement Learning with R

In this section, the basic concepts of reinforcement learning are addressed. This section helps you to understand the fundamental concepts and elements of reinforcement learning.

This section contains the following chapters:

  • Chapter 1Overview of Reinforcement Learning with R
  • Chapter 2Building Blocks of Reinforcement Learning

About the Author

  • Giuseppe Ciaburro

    Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master’s degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 18 years’ professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.

    Browse publications by this author

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