BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UC Irvine Donald Bren School of Information & Computer Sciences - ECPv6.3.4//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:UC Irvine Donald Bren School of Information & Computer Sciences
X-ORIGINAL-URL:https://ics.uci.edu
X-WR-CALDESC:Events for UC Irvine Donald Bren School of Information & Computer Sciences
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221110T160000
DTEND;TZID=America/Los_Angeles:20221110T170000
DTSTAMP:20260908T083600
CREATED:20221103T060946Z
LAST-MODIFIED:20221103T060946Z
UID:14729-1668096000-1668099600@ics.uci.edu
SUMMARY:Reinforcement Learning under Unmeasured Confounding
DESCRIPTION:In practical reinforcement learning (RL)\, a representation of the full state which makes the system Markovian and therefore amenable to most existing RL algorithms is not known a priori. Decision makers are often facing so-called partial observability of the state information\, which significantly hinders the task of RL. Motivated by recent advances in causal inference\, we study batch RL in the face of unmeasured confounders using auxiliary variables. A number of non-parametric identification results are established\, based on which several promising policy optimization algorithms are proposed with finite-sample regret guarantees. Further\, if time permits\, I will discuss the phenomenon named “blessing from experts” and introduce the framework of super reinforcement learning in the batch setting.
URL:https://ics.uci.edu/event/reinforcement-learning-under-unmeasured-confounding/
LOCATION:Donald Bren Hall\, Irvine\, CA\, 92697\, United States
CATEGORIES:Department Seminars
ATTACH;FMTTYPE=image/jpeg:https://ics.uci.edu/wp-content/uploads/2022/11/EmbeddedImage.jpeg
END:VEVENT
END:VCALENDAR