Date of Award

12-2014

Degree Type

Masters Thesis

Degree Name

Master of Science (MS)

Department

Computing

Committee Chair

Bikramjit Banerjee

Committee Chair Department

Computing

Committee Member 2

Beddhu Murali

Committee Member 2 Department

Computing

Committee Member 3

Dia Ali

Committee Member 3 Department

Computing

Abstract

This thesis describes the design and implementation of a Reinforcement Learning algorithm on a camera surveillance model which is used to know the stackelberg strategies of attacker and defender. This reinforcement learning algorithm is compared with the uniform policy and hill climbing algorithms by executing them on a common set of different data files, generated programmatically with various combinations of problem size, location, and orientation transitions as well as rewards of attacker and defender. The comparison includes the time taken to obtain better stackelberg policy and the resulted final pay-off of the defender. This thesis shows that the reinforcement learning algorithm developed in Java performs better than the uniform policy and proves to be chosen for large problem size as it produces acceptable results in less time when compared to that of the hill climbing algorithm.

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