This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.

Nominated by the Rensselaer Polytechnic Institute as an outstanding Ph.D. thesis Explains reinforcement learning through a range of problems by exploring what affects reinforcement learning and what contributes to a successful implementation Includes a contemporary design of experiments methods, comprising of a novel sequential experimentation procedure that finds convergent learning algorithm parameter subregions and stochastic kriging for response surface metamodeling Includes supplementary material: sn.pub/extras

Autorentext

Christopher Gatti received his PhD in Decision Sciences and Engineering Systems from Rensselaer Polytechnic Institute (RPI). During his time at RPI, his work focused on machine learning and statistics, with applications in reinforcement learning, graph search, stem cell RNA analysis, and neuro-electrophysiological signal analysis. Prior to beginning his graduate work at RPI, he received a BSE in mechanical engineering and an MSE in biomedical engineering, both from the University of Michigan. He then continued to work at the University of Michigan for three years doing computational biomechanics focusing on the shoulder and knee. He has been a gymnast since he was a child and is currently an acrobat for Cirque du Soleil.

Inhalt
Introduction.- Reinforcement Learning. Design of Experiments.- Methodology.- The Mountain Car Problem.- The Truck Backer-Upper Problem.- The Tandem Truck Backer-Upper Problem.- Appendices.