Hands-On Reinforcement Learning with R
- 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
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 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.
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?