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dc.creatorHooker, Julian Andrew
dc.date.available2011-02-18T21:15:33Z
dc.date.issued2004-05
dc.identifier.urihttp://hdl.handle.net/2346/15799en_US
dc.description.abstractSimulation against a model can greatly improve the learning rate of Reinforcement Learning. The Dyna algorithm uses both real experience and model learning to facilitate simulation. However, the model used in Dyna is fairly limited, yet still has some desirable properties. Examination of a few different known models can help bring to light ways of improving the Dyna model. Combining ideas from what is learned about these models should allow to a greatly improved model for Reinforcement Learning simulation.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.subjectReinforcement learning (Machine learning) -- Mathematical modelsen_US
dc.subjectComputer simulationen_US
dc.subjectArtificial intelligenceen_US
dc.titleOnline statistical modeling in reinforcement learning
dc.typeThesis
thesis.degree.nameM.S.
thesis.degree.levelMasters
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas Tech University
thesis.degree.departmentComputer Science
dc.degree.departmentComputer Scienceen_US
dc.rights.availabilityUnrestricted.


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