BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260803T153429
DTSTART;TZID=America/Detroit:20260909T160000
DTEND;TZID=America/Detroit:20260909T170000
SUMMARY:Workshop / Seminar:Stability\, Approximation\, and Robustness of Optimal Policies in POMDPs
DESCRIPTION:In this talk\, I will focus on the stability\, approximation\, and robustness of optimal policies in partially observable Markov decision processes (POMDPs) under discounted and average cost criteria.\n\nI will begin with filter kernel perturbation under model change. I will establish how small perturbations in the transition or observation kernels lead to explicit\, nonasymptotic bounds on both the filter kernel and the performance of the induced policies. A primary result demonstrates that an optimal policy computed under an incorrect model remains near optimal for the true model\, with quantitatively bounded errors.\n\nBuilding on these stability concepts\, I will then talk about the average cost setting and describe conditions under which the nonlinear filter exhibits a contraction property. Under these conditions\, the vanishing discounted approach yields a solution to the average cost optimality equation\, guaranteeing the existence of a stationary optimal policy. Furthermore\, this provides explicit bounds that quantify the influence of initial prior distribution errors and model errors on long run performance.\n\nFinally\, I will present results on implementable approximations. I will introduce refined error bounds for finite window controllers and conclude by discussing how Q learning can be utilized through either finite window or the quantization of the belief process to obtain near optimal policies for both discounted and average cost criteria.
UID:149581-21906722@events.umich.edu
URL:https://events.umich.edu/event/149581
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1360
CONTACT:
END:VEVENT
END:VCALENDAR