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Expert-Assisted Transfer Reinforcement Learning

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dc.contributor.advisor Friedler, Sorelle
dc.contributor.author Slack, Dylan
dc.date.accessioned 2019-08-21T16:18:34Z
dc.date.available 2019-08-21T16:18:34Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/10066/21630
dc.description.abstract Reinforcement Learning is concerned with developing machine learning approaches to answer the question: "What should I do?" Transfer Learning attempts to use previously trained Reinforcement Learning models to achieve better performance in new domains. There is relatively little work in using expert-advice to achieve better performance in the transfer of reinforcement learning models across domains. There is even less work that concerns the use of expert-advice in transfer deep reinforcement learning. This thesis presents a method that gives experts a route to incorporate their domain knowledge in the transfer learning process in deep reinforcement learning scenarios by presenting them with a decision set that they can edit. The decision set is then included in the learning process in the target domain. This work describes relevant background to both Reinforcement and Transfer Learning, provides the implementation of the proposed method, and suggests its usefulness and limitations by providing a series of applications.
dc.description.sponsorship Haverford College. Department of Computer Science
dc.language.iso eng
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
dc.subject.lcsh Reinforcement learning
dc.title Expert-Assisted Transfer Reinforcement Learning
dc.type Thesis
dc.rights.access Dark Archive until 2020-01-01, afterwards Open Access.


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http://creativecommons.org/licenses/by-nc/4.0/ Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/4.0/

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