Hands-On Reinforcement Learning with R: Get up to speed with building self-learning systems using R 3.x

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.

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?