PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 39
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorLee, Kiminko
dc.contributor.authorLaura Smithko
dc.contributor.authorPieter Abbeelko
dc.date.accessioned2024-01-29T07:00:27Z-
dc.date.available2024-01-29T07:00:27Z-
dc.date.created2024-01-29-
dc.date.created2024-01-29-
dc.date.issued2021-07-
dc.identifier.citation38th International Conference on Machine Learning, ICML 2021-
dc.identifier.urihttp://hdl.handle.net/10203/317945-
dc.publisherInternational Machine Learning Society (IMLS)-
dc.titlePEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationname38th International Conference on Machine Learning, ICML 2021-
dc.identifier.conferencecountryAU-
dc.contributor.localauthorLee, Kimin-
dc.contributor.nonIdAuthorLaura Smith-
dc.contributor.nonIdAuthorPieter Abbeel-
Appears in Collection
AI-Conference Papers(학술대회논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0