Hands-On Reinforcement Learning with R
Packt Publishing Limited (Verlag)
978-1-78961-671-2 (ISBN)
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Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym
Key Features
Explore the design principles of reinforcement learning and deep reinforcement learning models
Use dynamic programming to solve design issues related to building a self-learning system
Learn how to systematically implement reinforcement learning algorithms
Book Description
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.
What you will learn
Understand how to use MDP to manage complex scenarios
Solve classic reinforcement learning problems such as the multi-armed bandit model
Use dynamic programming for optimal policy searching
Adopt Monte Carlo methods for prediction
Apply TD learning to search for the best path
Use tabular Q-learning to control robots
Handle environments using the OpenAI library to simulate real-world applications
Develop deep Q-learning algorithms to improve model performance
Who this book is for
This book is for anyone who wants to learn about reinforcement learning with R from scratch. A solid understanding of R and basic knowledge of machine learning are necessary to grasp the topics covered in the book.
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 Universita 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.
Table of Contents
Overview of Reinforcement Learning with R
Building Blocks of Reinforcement Learning
Markov Decision Processes in Action
Multi-Armed Bandit Models
Dynamic programming for Optimal Policies
Monte-Carlo Methods for Prediction
Temporal Difference Learning
Reinforcement Learning in Game Applications
MAB for Financial Engineering
TD learning in HealthCare
Exploring Deep Reinforcement Learning methods
Deep Q learning Using Keras
Whats Next?
| Erscheinungsdatum | 20.12.2019 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-78961-671-9 / 1789616719 |
| ISBN-13 | 978-1-78961-671-2 / 9781789616712 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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