BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241015T110000
DTEND;TZID=America/New_York:20241015T120000
DTSTAMP:20260603T040752
CREATED:20240821T140955Z
LAST-MODIFIED:20240821T140955Z
UID:11970-1728990000-1728993600@seasevents.nmsdev7.com
SUMMARY:ESE Fall Seminar - "Learning-NUM: Utility Maximization in Stochastic Queueing Networks"
DESCRIPTION:We consider the problem of network utility maximization (NUM) and propose a new Learning-NUM framework\,  where the users’ utility functions are unknown apriori and the utility function values can be observed only after the corresponding traffic is delivered to the destination.   We start by considering linear utility functions and propose a priority-based network control policy\, that combines techniques from network control and multi-arm bandits to achieve logarithmic regret.  We then consider the case of concave utility functions and design the Gradient Sampling Max-Weight algorithm (GSMW)\, based on the ideas of gradient estimation and Max-Weight scheduling\, that achieves sublinear utility regret.  We further demonstrate the applicability of the gradient sampling approach to minimum delay routing in wireless networks. Finally\, we consider the general problem of reinforcement learning for queueing networks with unbounded state-spaces\, with the goal of making control decisions that minimizing the queue length.  We formulate the problem as an MDP\, and propose a new reinforcement learning framework\, called Truncated Upper Confidence Reinforcement Learning (TUCRL)\, that  can achieve optimal performance.  We show how this framework can be applied to deep reinforcement learning (DRL) for online stochastic network optimization.
URL:https://seasevents.nmsdev7.com/event/ese-fall-seminar-title-tbd-16/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
END:VCALENDAR