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Frontiers in Inverse Reinforcement Learning

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dc.contributor.advisor Gavenciak, Tomas en_US
dc.contributor.author SARKAR, SAYAN en_US
dc.date.accessioned 2020-07-13T04:01:18Z
dc.date.available 2020-07-13T04:01:18Z
dc.date.issued 2019-11 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4877
dc.description.abstract How do we teach machines to do something that we can perform reasonably well, but cannot easily express as a utility maximization problem? Can machines learn underlying utility of a domain from many human demonstrations? The goal of the field of Inverse Reinforcement Learning (IRL) is to infer the crux of the goal of a domain from expert (human) demonstrations. This thesis categorically surveys the current IRL literature with a formal introduciton and motivation for the problem. We discuss the central challenges of the domain and expound upon how different algorithms deal with the challenges. We propose an reformulation of the IRL problem by including ranked set of trajectories of different levels of expert capability and discuss how that might lead towards a new set of algorithms in the field, motivated by some very recently developed approaches. We conclude with discussing some broad advances in the research area and possibilities for further extension. en_US
dc.language.iso en en_US
dc.subject Mathematics en_US
dc.subject 2020 en_US
dc.title Frontiers in Inverse Reinforcement Learning en_US
dc.type Thesis en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Mathematics en_US
dc.contributor.registration 20141132 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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