Date of Award
Fall 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.
Copyright
2014, Madhavi Chittireddy
Recommended Citation
Chittireddy, Madhavi, "Reinforcement Learning of Distributed Surveillance Plans" (2014). Master's Theses. 75.
https://aquila.usm.edu/masters_theses/75