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dc.contributor.authorSeymour, Benjamin
dc.contributor.authorLee, Sang Wan
dc.date.accessioned2019-02-12T00:33:21Z
dc.date.available2019-02-12T00:33:21Z
dc.date.issued2019
dc.identifier.issn2352-1546
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/289265
dc.description.abstractReinforcement Learning describes a general method for trial-and-error learning, and has emerged as a dominant framework both for optimal control in autonomous robots, and understanding decision-making in the brain. Despite their common roots, however, these two fields have evolved largely independently. In this perspective we consider how each now face problems that could potentially be addressed by insights from the other, and argue that an interdisciplinary approach could greatly accelerate progress in both.
dc.publisherElsevier Limited
dc.titleDecision-making in brains and robots - the case for an interdisciplinary approach
dc.typeArticle
prism.endingPage145
prism.publicationNameCurrent Opinion in Behavioral Sciences
prism.startingPage137
prism.volume26
dc.identifier.doi10.17863/CAM.36527
dcterms.dateAccepted2018-12-22
rioxxterms.versionofrecord10.1016/j.cobeha.2018.12.012
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2018-12-22
dc.contributor.orcidSeymour, Benjamin [0000-0003-1724-5832]
dc.identifier.eissn2352-1546
rioxxterms.typeJournal Article/Review
pubs.funder-project-idWellcome Trust (097490/Z/11/Z)
pubs.funder-project-idArthritis Research UK (21192)
pubs.funder-project-idArthritis Research UK (21537)
cam.issuedOnline2019-02-05
cam.orpheus.successThu Jan 30 10:52:07 GMT 2020 - Embargo updated
rioxxterms.freetoread.startdate2020-02-05


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