This is the code repository for Hands-On Reinforcement Learning with R, published by Packt.
Get up to speed with building self-learning systems using R 3.x
Reinforcement Learning is an exciting part of machine learning. It has uses in technology from autonomous cars to game playing, and creates algorithms that can adapt to environmental changes. This book helps to understand how to implement RL with R, and explores interesting practical examples, such as using tabular Q-learning to control robots.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
Xtrain <- MnistData$train$x
Ytrain <- MnistData$train$y
Xtest <- MnistData$test$x
Ytest <- MnistData$test$y
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| All | R | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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.
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