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dc.contributor.advisor | Huber, Manfred | |
dc.creator | Bose, Sourabh | |
dc.date.accessioned | 2019-05-28T20:03:49Z | |
dc.date.available | 2019-05-28T20:03:49Z | |
dc.date.created | 2019-05 | |
dc.date.issued | 2019-05-09 | |
dc.date.submitted | May 2019 | |
dc.identifier.uri | http://hdl.handle.net/10106/28094 | |
dc.description.abstract | The framework of reinforcement learning is a powerful suite of algorithms that can learn generalized solutions to complex decision making problems. However, the applications of reinforcement learning algorithms to traditional machine learning problems such as clustering, classification and representation learning, have rarely been explored. With the advent of large amounts of data, robust models are required which can extract meaningful representations from the data that can potentially be applied to new unseen tasks. The presented work investigates the applications of reinforcement learning algorithms in the perspective of transfer learning by applying algorithms in the framework of reinforcement learning to address a variety of machine learning problems in order to learn concise abstractions useful for transfer. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.subject | Machine learning | |
dc.subject | Reinforcement learning | |
dc.subject | Artificial neural networks | |
dc.subject | Representation learning | |
dc.title | Learning Representations Using Reinforcement Learning | |
dc.type | Thesis | |
dc.degree.department | Computer Science and Engineering | |
dc.degree.name | Doctor of Philosophy in Computer Science | |
dc.date.updated | 2019-05-28T20:04:55Z | |
thesis.degree.department | Computer Science and Engineering | |
thesis.degree.grantor | The University of Texas at Arlington | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Doctor of Philosophy in Computer Science | |
dc.type.material | text | |
dc.creator.orcid | 0000-0002-1504-8942 | |
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