Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/477
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dc.contributor.authorBozic, Dordeen_US
dc.contributor.authorTadic, Predragen_US
dc.contributor.authorNikolić, Mladenen_US
dc.date.accessioned2022-08-13T09:51:52Z-
dc.date.available2022-08-13T09:51:52Z-
dc.date.issued2021-01-01-
dc.identifier.isbn9781665425841-
dc.identifier.urihttps://research.matf.bg.ac.rs/handle/123456789/477-
dc.description.abstractOptions represent a framework for reasoning across multiple time scales in reinforcement learning (RL). With the recent active interest in the unsupervised learning paradigm in the RL research community, the option framework was adapted to utilize the concept of empowerment, which corresponds to the amount of influence the agent has on the environment and it's ability to perceive this influence, and which can be optimized without any supervision provided by the environment's reward structure. Many recent papers modify this concept in various ways achieving commendable results. Through these various modifications, however, the initial context of empowerment is often lost. In this work we offer a comparative study of such papers through the lens of the original empowerment principle.en
dc.relation.ispartof2021 29th Telecommunications Forum, TELFOR 2021 - Proceedingsen_US
dc.subjectEmpowermenten
dc.subjectExplorationen
dc.subjectOption frameworken
dc.subjectReinforcement learningen
dc.titleIntrinsically motivated option learning: A comparative study of recent methodsen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1109/TELFOR52709.2021.9653226-
dc.identifier.scopus2-s2.0-85124589844-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85124589844-
dc.contributor.affiliationInformatics and Computer Scienceen_US
dc.description.rankM33en_US
dc.relation.firstpage1en_US
dc.relation.lastpage4en_US
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairetypeConference Paper-
crisitem.author.deptInformatics and Computer Science-
Appears in Collections:Research outputs
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