Term of Award

Spring 2019

Degree Name

Master of Science in Mathematics (M.S.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Mathematical Sciences

Committee Chair

Stephen Carden

Committee Member 1

Ionut Emil Iacob

Committee Member 2

Scott Kersey

Committee Member 3

Divine Wanduku

Committee Member 3 Email



Part of the implementation of Reinforcement Learning is constructing a regression of values against states and actions and using that regression model to optimize over actions for a given state. One such common regression technique is that of a decision tree; or in the case of continuous input, a regression tree. In such a case, we fix the states and optimize over actions; however, standard regression trees do not easily optimize over a subset of the input variables\cite{Card1993}. The technique we propose in this thesis is a hybrid of regression trees and kernel regression. First, a regression tree splits over state variables at a macro level, then kernel regression models the effects of actions with a smooth function at a micro level. Then non-linear optimization is used to optimize the kernel regressed function to find the best action and get a precise prediction of its value for any given state. This ``best action" is then stored in the tree and is instantly retrieved upon making decisions. This is not only more appropriate for problems with continuous output, but also for problems with a discrete output since it also generalizes the knowledge over actions as well as states, providing for smarter decision-making. The capabilities of this technique are observed for a time series constructed to realistically model a stock problem.

OCLC Number


Research Data and Supplementary Material


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