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A survey of inverse reinforcement learning

dc.contributor.authorAdams, Stephenen
dc.contributor.authorCody, Tyleren
dc.contributor.authorBeling, Peter A.en
dc.date.accessioned2022-07-29T14:17:21Zen
dc.date.available2022-07-29T14:17:21Zen
dc.date.issued2022-08en
dc.description.abstractLearning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision process from examples provided by the teacher. The reward function is often considered the most succinct description of a task. In simple applications, the reward function may be known or easily derived from properties of the system and hard coded into the learning process. However, in complex applications, this may not be possible, and it may be easier to learn the reward function by observing the actions of the teacher. This paper provides a comprehensive survey of the literature on IRL. This survey outlines the differences between IRL and two similar methods - apprenticeship learning and inverse optimal control. Further, this survey organizes the IRL literature based on the principal method, describes applications of IRL algorithms, and provides areas of future research.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s10462-021-10108-xen
dc.identifier.eissn1573-7462en
dc.identifier.issn0269-2821en
dc.identifier.urihttp://hdl.handle.net/10919/111402en
dc.language.isoenen
dc.publisherSpringeren
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectReinforcement learningen
dc.subjectInverse reinforcement learningen
dc.subjectInverse optimal controlen
dc.subjectApprenticeship learningen
dc.subjectLearning from demonstrationen
dc.titleA survey of inverse reinforcement learningen
dc.title.serialArtificial Intelligence Reviewen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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