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DTSTART:20260308T030000
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BEGIN:VEVENT
DTSTAMP;TZID=America/Chicago:20260831T160400
UID:224464@calendar.wisc.edu
DTSTART;TZID=America/Chicago:20260903T130000
DTEND;TZID=America/Chicago:20260903T140000
DESCRIPTION:Lan Wang: New Results for Distributional Reinforcement Learning
 . Distributional reinforcement learning (RL) models the entire distributio
 n of returns and is particularly useful for risk-sensitive decision-making
 . Quantile temporal difference (QTD) learning is a widely used model-free 
 distributional RL method with strong empirical performance\, yet its theor
 etical guarantees remain less developed. We provide nonasymptotic performa
 nce guarantees for QTD.
LOCATION:7560 Morgridge Hall
SUMMARY:Statistics Seminar
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