Reinforcement Learning Techniques with R [Video]

More Information
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
About

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.

Style and Approach

This video helps you to understand Reinforcement Learning by following simple instructions in step-by–step, easy-to-follow techniques and programs.

Features
  •  Understand what Reinforcement Learning can do for you
  • Reinforcement Learning will help you calculate probable successful outcomes by implementing the Stimulus - Action - Reward paradigm and programming the environment
Course Length 2 hours 21 minutes
ISBN 9781788390705
Date Of Publication 27 Jun 2017
R Example – Updating Optimal Policy Navigating 2 x 2 Grid
More MDPtoolbox Function Examples Using R
R Example – Finding Optimal 3 x 4 Grid World Policy
R Exercise – Building a 3 x 4 Grid World Environment
R Exercise Solution – Building a 3 x 4 Grid World Environment

Authors

Dr. Geoffrey Hubona

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.