Self-Supervised Adversarial Imitation Learning

dc.contributor.author	Monteiro, Juarez
dc.contributor.author	Gavenski, Nathan
dc.contributor.author	Meneguzzi, Felipe
dc.contributor.author	Barros, Rodrigo C.
dc.contributor.institution	University of Aberdeen.Computing Science	en
dc.date.accessioned	2023-08-23T11:05:01Z
dc.date.available	2023-08-23T11:05:01Z
dc.date.issued	2023-04-21
dc.description	This paper has been accepted in the International Joint Conference on Neural Networks (IJCNN) 2023 This work was supported by UK Research and Innovation [grant number EP/S023356/1], in the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence (www.safeandtrustedai.org) and made possible via King’s Computational Research, Engineering and Technology Environment (CREATE) [27].	en
dc.format.extent	8
dc.format.extent	405989
dc.identifier	276845980
dc.identifier	2940bcc4-2d72-4bb1-914d-0bbb7a573b8f
dc.identifier.citation	Monteiro, J, Gavenski, N, Meneguzzi, F & Barros, R C 2023 'Self-Supervised Adversarial Imitation Learning' ArXiv. https://doi.org/10.48550/arXiv.2304.10914	en
dc.identifier.doi	10.48550/arXiv.2304.10914
dc.identifier.other	ArXiv: http://arxiv.org/abs/2304.10914v1
dc.identifier.other	ORCID: /0000-0003-3549-6168/work/141302256
dc.identifier.uri	https://hdl.handle.net/2164/21527
dc.language.iso	eng
dc.publisher	ArXiv
dc.subject	cs.LG	en
dc.subject	cs.AI	en
dc.subject	UK Research and Innovation (UKRI)	en
dc.subject	EP/S023356/1	en
dc.title	Self-Supervised Adversarial Imitation Learning	en
dc.type	Preprint	en

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