Design of experiments for reinforcement learning
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Authors
Gatti, Christopher J.
Issue Date
2014-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Decision sciences and engineering systems
Alternative Title
Abstract
The purpose of this work is to gain a better understanding of reinforcement learning in a variety of problems. We are interested in fundamental knowledge, basic science if you will, of what affects reinforcement learning and what contributes to a successful implementation. This work takes an empirical approach to understanding the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of the learned knowledge. We study these entities using an approach that is not commonly employed to study machine learning methods, that being design of experiments. We use contemporary design of experiments methods, including a novel sequential experimentation procedure that finds convergent learning algorithm parameter subregions, and stochastic kriging for response surface metamodeling. We consider three problems in this work, one benchmark reinforcement learning problem and two more real world and challenging problems: the mountain car problem, the truck backer-upper problem, and the tandem truck backer-upper problem. The knowledge gained from this work provides 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.
Description
August 2014
School of Engineering
School of Engineering
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY