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DTSTART:20240310T100000
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DTSTART;TZID=America/Los_Angeles:20240209T133000
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DTSTAMP:20260514T210331
CREATED:20240318T212503Z
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SUMMARY:Policy Optimization with Compatible Mirror Approximation
DESCRIPTION:Speaker Bio: Zhihan is a fourth-year PhD student in the Paul G. Allen School of Computer Science & Engineering at University of Washington\, advised by Prof. Maryam Fazel. His research interests are broadly in statistics\, optimization and machine learning. \n\n\nAbstract: We propose Compatible Mirror Policy Optimization (CoMPO)\, a framework that incorporates general function approximation into policy mirror descent methods. In contrast to the popular approach of using the $L_2$ norm to measure function approximation errors (regardless of the mirror map)\, CoMPO uses the Bregman divergence induced by the specific mirror map for policy projection. Such a compatibility bridges the gap between theory and practice: not only does it achieve fast linear convergence with general function approximation\, but it also includes several well-known practical methods as special cases\, immediately providing them strong convergence guarantees.
URL:https://ifds.info/event/policy-optimization-with-compatible-mirror-approximation/
LOCATION:Zoom
CATEGORIES:MLOpt@UWash
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