Reinforcement Learning Techniques with R
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Reinforcement Learning Techniques with R

Learn how to implement Reinforcement Learning techniques using the R programming language
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
7 students enrolled
Created by Packt Publishing
Last updated 7/2017
English
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Current price: $10 Original price: $125 Discount: 92% off
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Includes:
  • 2.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Get to know what Reinforcement Learning is
  • Use Reinforcement Learning to implement MDPToolbox
  • Understand and Implement the "Grid World" Problem in R
  • Generate a Random MDP Problem with R
  • Learn how to use MDPtoolbox
  • Categorize MDPtoolbox R functions
  • Work with R examples using MDPtoolbox functions
View Curriculum
Requirements
  • You should know how to program in R, but prior experience in Reinforcement Learning is not required.
Description

Reinforcement Learning is a type of machine learning that allows machines and software agents to act smart and automatically detect the ideal behavior within a specific environment, in order to maximize its performance and productivity. Reinforcement Learning is becoming popular because it not only serves as an way to study how machine and software agents learn to act, it is also been used as a tool for constructing autonomous systems that improve themselves with experience. This video will give you a brief introduction to Reinforcement Learning; it will help you navigate the "Grid world" to calculate likely successful outcomes using the popular MDPToolbox package. This video will show you how the Stimulus - Action - Reward algorithm works in Reinforcement Learning. By the end of this video you will have a basic understanding of the concept of reinforcement learning, you will have compiled your first Reinforcement Learning program, and will have mastered programming the environment for Reinforcement Learning.

About the author :

Dr. Geoffrey Hubona held a full-time tenure-track, and 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.

Who is the target audience?
  • This course is intended for anyone who wants to learn about Reinforcement Learning using the R language.
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Curriculum For This Course
11 Lectures
02:21:01
+
What Reinforcement Learning Can Do for You
4 Lectures 43:28

This video provides an overview of the entire course.

Preview 03:57

The aim of this video is to introduce Reinforcement Learning (RL) and illustrate RL concepts with a prototypical example.

Understanding the RL “Grid World” Problem
07:06

The aim of this video is to demonstrate how to represent Grid World using the R software and to introduce the RL concepts of sequences of actions and randomness of actions.

Implementing the Grid World Framework in R
16:52

The aim is twofold: one isto probe more deeply into how the possible random execution of actions can affect the outcome, andthe second is to demonstrate that the specific reward structure can affect the optimal policy with regard to the best action.

Navigating Grid World and Calculating Likely Successful Outcomes
15:33
+
Your First Reinforcement Learning Program
2 Lectures 45:16

The video deals with developing the optimal policy as a model-free solution to navigating a 2 x 2 grid

Preview 26:10

This video addresses the epsilon-greedy action selection strategy to update the optimal policy with a model-free solution to navigating a 2 x 2 grid.

R Example – Updating Optimal Policy Navigating 2 x 2 Grid
19:06
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Programming the Environment
5 Lectures 52:17
This video deals with using the R MDPtoolbox package to find the optimal policy solution for navigating a 2 x 2 grid.
Preview 17:51

This video identifies and demonstrates several of the more important MDPtoolbox functions as pertinent to Reinforcement Learning problems.
More MDPtoolbox Function Examples Using R
10:14

This video closes the loop on representing the 3 x 4 Grid World RL problem using R and without using any RL-specific R packages.
R Example – Finding Optimal 3 x 4 Grid World Policy
10:30

This video presents an end-of-Title user exercise, integrating much of the material presented in the three sections.

R Exercise – Building a 3 x 4 Grid World Environment
03:27

This video present a solution to the end-of-Title user exercise presented in the preceding video.
R Exercise Solution – Building a 3 x 4 Grid World Environment
10:15
About the Instructor
Packt Publishing
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