Learning Path: R: Reward-Based Learning With R

Tackle programming problems and explore model-based and model-free learning algorithms for reward-based learning in R

What you'll learn

Get to know the nuts and bolts of writing R code in RStudio

Get a tour of the most important data structures in R

Execute environment and Q-Learning functions with R

Learn episode and state-action functions in R

Master Q-Learning with Greedy Selection examples in R

Explore simulated annealing changed discount factor through examples in R

Requirements

Basic programming knowledge

Basic knowledge of math and statistics would be beneficial

Description

R is a high-level statistical language and is widely used among statisticians and data miners to develop statistical applications. If you want to learn reward-based learning with R, then you should surely go for this Learning Path.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.






The highlights of this Learning Path are:


Tour of the most important data structures in RImplement applications for model-based and model-free RL
Beginning with the basics of R programming, this Learning Path provides step-by-step resources and time-saving methods to help you solve programming problems efficiently. You will be able to boost your productivity with the most popular R packages and data structures such as matrices, lists, and factors. You will be able to tackle issues with data input/output and will learn to work with strings and dates.


Moving ahead, you will know the differences in model-free and model-based approaches to reinforcement learning. This Learning Path discusses the characteristics, advantages and disadvantages, and typical examples of model-free and model-based approaches.You will learn Monte Carlo approach, Q-Learning approach, SARSA approach, and many more. Finally, you will take a look at model-free simulated annealing and more Q-Learning algorithms.


By the end of this Learning Path, you will be able to build actions, rewards, and punishments through these models in R for reinforcement learning.

About the Author
For this course, we have the best works of this esteemed authors:
Dr David Wilkins is a biologist with nearly a decade of experience writing R for research applications, particularly high-throughput analysis of genetic data. He has also developed a number of open source R packages.Dr. Geoffrey Hubona held a full-time tenure-track, tenured, assistant and associate professor faculty positions at three major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. Dr. Hubona earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA.

Overview

Section 1: Learn R programming

Lecture 1 The Course Overview

Lecture 2 Setting Up RStudio

Lecture 3 Writing, Running, and Saving R Scripts

Lecture 4 Exploring Numbers and Arithmetic Operators

Lecture 5 Working with Variables and Vectors

Lecture 6 Using Functions and Reading Function Documentation

Lecture 7 Exploring Vectors in Depth and Understanding Data Types

Lecture 8 Working with Matrices and Arrays

Lecture 9 Discovering Lists

Lecture 10 Discovering Data Frames

Lecture 11 Exploring Factors

Lecture 12 Reading Data from a File

Lecture 13 Subsetting Data Frames

Lecture 14 Statistical Summaries of Data

Lecture 15 Statistical Tests on Data

Lecture 16 Manipulating Data

Lecture 17 Writing Data to File

Section 2: Discover Algorithms for Reward-Based Learning in R

Lecture 18 The Course Overview

Lecture 19 R Example – Building Model-Free Environment

Lecture 20 R Example – Finding Model-Free Policy

Lecture 21 R Example – Finding Model-Free Policy (Continued)

Lecture 22 R Example – Validating Model-Free Policy

Lecture 23 Policy Evaluation and Iteration

Lecture 24 R Example – Moving a Pawn with Changed Parameters

Lecture 25 Discount Factor and Policy Improvement

Lecture 26 Monte Carlo Methods

Lecture 27 Environment and Q-Learning Functions with R

Lecture 28 Learning Episode and State-Action Functions in R

Lecture 29 State-Action-Reward-State-Action (SARSA)

Lecture 30 Simulated Annealing – An Alternative to Q-Learning

Lecture 31 Q-Learning with a Discount Factor

Lecture 32 Visual Q-Learning Examples

This Learning Path is for programmers, data analyst, or data science enthusiasts who want to learn reward-based learning with R. No prior R knowledge is required as the Learning Path covers the fundamental concepts of R.