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dc.contributor.supervisorPeters, James F. (Electrical and Computer Engineering)en
dc.contributor.authorHenry, Christopher
dc.date.accessioned2006-09-19T14:27:49Z
dc.date.available2006-09-19T14:27:49Z
dc.date.issued2006-09-19T14:27:49Z
dc.identifier.urihttp://hdl.handle.net/1993/289
dc.description.abstractThis thesis presents a rough set approach to reinforcement learning. This is made possible by considering behaviour patterns of learning agents in the context of approximation spaces. Rough set theory introduced by Zdzisław Pawlak in the early 1980s provides a ground for deriving pattern-based rewards within approximation spaces. Learning can be considered episodic. The framework provided by an approximation space makes it possible to derive pattern-based reference rewards at the end of each episode. Reference rewards provide a standard for reinforcement comparison as well as the actor-critic method of reinforcement learning. In addition, approximation spaces provide a basis for deriving episodic weights that provide a basis for a new form of off-policy Monte Carlo learning control method. A number of conventional and pattern-based reinforcement learning methods are investigated in this thesis. In addition, this thesis introduces two learning environments used to compare the algorithms. The first is a Monocular Vision System used to track a moving target. The second is an artificial ecosystem testbed that makes it possible to study swarm behaviour by collections of biologically-inspired bots. The simulated ecosystem has an ethological basis inspired by the work of Niko Tinbergen, who introduced in the 1960s methods of observing and explaining the behaviour of biological organisms that carry over into the study of the behaviour of interacting robotic devices that cooperate to survive and to carry out highly specialized tasks. Agent behaviour during each episode is recorded in a decision table called an ethogram, which records features such as states, proximate causes, responses (actions), action preferences, rewards and decisions (actions chosen and actions rejected). At all times an agent follows a policy that maps perceived states of the environment to actions. The goal of the learning algorithms is to find an optimal policy in a non-stationary environment. The results of the learning experiments with seven forms of reinforcement learning are given. The contribution of this thesis is a comprehensive introduction to a pattern-based evaluation of behaviour during reinforcement learning using approximation spaces.en
dc.format.extent1337562 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectApproximation spaceen
dc.subjectEcosystemen
dc.subjectEthologyen
dc.subjectReinforcement Learningen
dc.subjectRough Setsen
dc.subjectSwarmen
dc.subjectTarget Trackingen
dc.titleReinforcement learning in biologically-inspired collective robotics: a rough set approachen
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typemaster thesisen_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.examiningcommitteeGunderson, David S. (Mathematics) Fazel-Rezai, Reza (Electrical and Computer Engineering)en
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteMay 2006en


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