
Reinforcement Learning with R
Ruben Oliva Ramos(Author)
Packt Publishing
Published on 28. February 2018
Book
Paperback/Softback
423 pages
978-1-78862-294-3 (ISBN)
Description
Exploring how software agents act in our surroundings leveraging the power of R
About This Book
* Learn how to deal with the most-common reinforcement learning problems with the best explained practical approach.
* Fast paced guide to have a better understanding to know everything about RL concepts, framewords, algorithms and many more.
* Deep dive and learn how to use popular MDPtoolbox package to its maximum extend.
Who This Book Is For
This book is intended for machine learning developers and enthusiasts who wants to learn about reinforcement learning and how it plays a major role in different domains. This book will take you from scratch and extent your knowledge and the possibilities in learning more about RL, important concepts and the problems associated with it
What You Will Learn
* Explore the framework, elements and framework of RL
* Find the resources available for building RL frameworks
* Run RL based algorithms on your own with sample examples provided, followed by customized exercises.
* How to formulate models for the environment
* Agent based models, Environment interactions, RL formulation (rewards, states, policy, action), Exploration v/s Exploitation, Decision making, Optimization
* Most recent libraries and packages in R (on RL elements)
* How to define and evaluation policies with specific mathematical formulation
* Devise the value functions in a mathematical formulation, and learn the various methodologies/algorithms for the evaluation of policies
* How RL is different from other supervised/unsupervised algorithms
In Detail
Reinforcement learning(RL) allows machines and software agents to act smart and automatically detect the ideal behavior within a specific surrounding, to maximize its performance and productivity. Reinforcement learning is becoming popular and is used as a tool for constructing autonomous systems that improve themselves with experience.
This book will give you a rundown on a brief introduction to reinforcement learning, using popular MDPtoolbox package. We will break the RL framework into its core building blocks, and provide you with details of each of the elements. In this journey you will see, common RL problems like Multi-Armed Bandit problem, types of RL learning algorithms, Markov Decision Processes (MDPs), monte carlo, dynamic programming such as policy and value iteration. Next you will identify temporal difference learnings such as Q-learning and SARSA. You will then learn, that, the utilization of various algorithms in each of these building blocks is kept secondary, as this research area is still open to better algorithms. We will take a practical and simple approach towards explaining the various building blocks of RL, and then bring them together to create a solution.
By the end of this book you will be able to write his/her own codes to construct self-learning autonomous systems. You will finally see, how reinforcement learning plays a big role in computer oriented games such as chess or tic-tac-toe agent.
About This Book
* Learn how to deal with the most-common reinforcement learning problems with the best explained practical approach.
* Fast paced guide to have a better understanding to know everything about RL concepts, framewords, algorithms and many more.
* Deep dive and learn how to use popular MDPtoolbox package to its maximum extend.
Who This Book Is For
This book is intended for machine learning developers and enthusiasts who wants to learn about reinforcement learning and how it plays a major role in different domains. This book will take you from scratch and extent your knowledge and the possibilities in learning more about RL, important concepts and the problems associated with it
What You Will Learn
* Explore the framework, elements and framework of RL
* Find the resources available for building RL frameworks
* Run RL based algorithms on your own with sample examples provided, followed by customized exercises.
* How to formulate models for the environment
* Agent based models, Environment interactions, RL formulation (rewards, states, policy, action), Exploration v/s Exploitation, Decision making, Optimization
* Most recent libraries and packages in R (on RL elements)
* How to define and evaluation policies with specific mathematical formulation
* Devise the value functions in a mathematical formulation, and learn the various methodologies/algorithms for the evaluation of policies
* How RL is different from other supervised/unsupervised algorithms
In Detail
Reinforcement learning(RL) allows machines and software agents to act smart and automatically detect the ideal behavior within a specific surrounding, to maximize its performance and productivity. Reinforcement learning is becoming popular and is used as a tool for constructing autonomous systems that improve themselves with experience.
This book will give you a rundown on a brief introduction to reinforcement learning, using popular MDPtoolbox package. We will break the RL framework into its core building blocks, and provide you with details of each of the elements. In this journey you will see, common RL problems like Multi-Armed Bandit problem, types of RL learning algorithms, Markov Decision Processes (MDPs), monte carlo, dynamic programming such as policy and value iteration. Next you will identify temporal difference learnings such as Q-learning and SARSA. You will then learn, that, the utilization of various algorithms in each of these building blocks is kept secondary, as this research area is still open to better algorithms. We will take a practical and simple approach towards explaining the various building blocks of RL, and then bring them together to create a solution.
By the end of this book you will be able to write his/her own codes to construct self-learning autonomous systems. You will finally see, how reinforcement learning plays a big role in computer oriented games such as chess or tic-tac-toe agent.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-78862-294-3 (9781788622943)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Ruben Oliva Ramos is a computer systems engineer, with a master's degree in computer and electronic systems engineering, teleinformatics and networking specialization from University of Salle Bajio in Leon, Guanajuato Mexico. He has more than five years of experience in: developing web applications to control and monitor devices connected with Arduino and Raspberry Pi using web frameworks and cloud services to build Internet of Things applications.
He is a mechatronics teacher at University of Salle Bajio and teaches students on the master's degree in Design and Engineering of Mechatronics Systems. He also works at Centro de Bachillerato Tecnologico Industrial 225 in Leon, Guanajuato Mexico, teaching the following: electronics, robotics and control, automation, and microcontrollers at Mechatronics Technician Career. He has worked on consultant and developer projects in areas such as monitoring systems and datalogger data using technologies such as Android, iOS, Windows Phone, Visual Studio .NET, HTML5, PHP, CSS, Ajax, JavaScript, Angular, ASP .NET databases (SQlite, mongoDB, and MySQL), and web servers (Node.js and IIS). Ruben has done hardware programming on Arduino, Raspberry Pi, Ethernet Shield, GPS and GSM/GPRS, ESP8266, and control and monitor systems for data acquisition and programming.
He is a mechatronics teacher at University of Salle Bajio and teaches students on the master's degree in Design and Engineering of Mechatronics Systems. He also works at Centro de Bachillerato Tecnologico Industrial 225 in Leon, Guanajuato Mexico, teaching the following: electronics, robotics and control, automation, and microcontrollers at Mechatronics Technician Career. He has worked on consultant and developer projects in areas such as monitoring systems and datalogger data using technologies such as Android, iOS, Windows Phone, Visual Studio .NET, HTML5, PHP, CSS, Ajax, JavaScript, Angular, ASP .NET databases (SQlite, mongoDB, and MySQL), and web servers (Node.js and IIS). Ruben has done hardware programming on Arduino, Raspberry Pi, Ethernet Shield, GPS and GSM/GPRS, ESP8266, and control and monitor systems for data acquisition and programming.