Learning Path: R: Reward-Based Learning with R

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:

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.