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 R
- Implement 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.
