Reinforcement Learning Techniques with R [Video]

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Reinforcement Learning Techniques with R [Video]

Dr. Geoffrey Hubona

Learn how to implement Reinforcement Learning techniques using the R programming language
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Video Details

ISBN 139781788390705
Course Length2 hours and 21 minutes

Video 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.

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.

Table of Contents

What Reinforcement Learning Can Do for You
The Course Overview
Understanding the RL “Grid World” Problem
Implementing the Grid World Framework in R
Navigating Grid World and Calculating Likely Successful Outcomes
Your First Reinforcement Learning Program
R Example – Finding Optimal Policy Navigating 2 x 2 Grid
R Example – Updating Optimal Policy Navigating 2 x 2 Grid
Programming the Environment
R Example – MDPtoolbox Solution 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

What You Will 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

Authors

Table of Contents

What Reinforcement Learning Can Do for You
The Course Overview
Understanding the RL “Grid World” Problem
Implementing the Grid World Framework in R
Navigating Grid World and Calculating Likely Successful Outcomes
Your First Reinforcement Learning Program
R Example – Finding Optimal Policy Navigating 2 x 2 Grid
R Example – Updating Optimal Policy Navigating 2 x 2 Grid
Programming the Environment
R Example – MDPtoolbox Solution 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

Video Details

ISBN 139781788390705
Course Length2 hours and 21 minutes
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