Full Text Available

Note: Clicking the button above will open the full text document at the original institutional repository in a new window.

Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning

Saved in:
Bibliographic Details
Published in:ArXiv cs.LG Recent Papers
Format: Online Article RSS Article
Published: 2026
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1864583773378576386
collection WordPress RSS
FRELIP Feed Integration
container_title ArXiv cs.LG Recent Papers
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:50039
institution FRELIP
journal_source_facet ArXiv cs.LG Recent Papers
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
sub_discipline_display Petroleum & Energy
sub_discipline_facet Petroleum & Energy
subject_display ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
subject_facet ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
title Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
title_auth Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
title_full Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
title_fullStr Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
title_full_unstemmed Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
title_short Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
title_sort unified framework of distributional regret in multi-armed bandits and reinforcement learning
topic ArXiv cs.LG Recent Papers
Petroleum & Energy
Engineering & Technology
url https://arxiv.org/abs/2605.05102v1