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.
","abstract_html":"<p>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. </p>","abstract_has_math":false,"creators":["Chittireddy, Madhavi"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":"Computing","degree_department":null,"school":null,"contributors":["Bikramjit Banerjee","Beddhu Murali","Dia Ali"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-01T08:00:00Z","date_published":"2014-12-01T08:00:00Z","updated_at":"2026-07-24T05:44:27Z","subjects":["reinforcement learning","stackleberg games","multiagent","attacker and defender","randomized strategies","AMPL","Artificial Intelligence and Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/75","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bikramjit Banerjee","Beddhu Murali","Dia Ali"]},{"key":"dc:creator","label":"Author","values":["Chittireddy, Madhavi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-02-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computing"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["reinforcement learning","stackleberg games","multiagent","attacker and defender","randomized strategies","AMPL","Artificial Intelligence and Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/75"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Reinforcement Learning of Distributed Surveillance Plans"]}]}],"canonical_facts":{"dc:contributor":["Bikramjit Banerjee","Beddhu Murali","Dia Ali"],"dc:creator":["Chittireddy, Madhavi"],"dc:date.available":["2014-02-01T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://aquila.usm.edu/masters_theses/75"],"dc:subject":["reinforcement learning","stackleberg games","multiagent","attacker and defender","randomized strategies","AMPL","Artificial Intelligence and Robotics"],"dc:title":["Reinforcement Learning of Distributed Surveillance Plans"],"thesis:degree_discipline":["Computing"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:44:27Z"}