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X-WR-CALNAME:Rethinking the Objective for Policy Optimization in Reinforcem
 ent Learning - Martha White\, Associate Professor\, University of Alberta
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TZUNTIL:20220313T100000Z
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DTSTART:20191103T020000
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RDATE:20201101T020000
RDATE:20211107T020000
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CREATED:20200529T190730Z
DESCRIPTION:Please register for this event here Abstract: The goal in reinf
 orcement learning is to obtain a policy that maximizes long-term reward. P
 olicy optimization in reinforcement learning involves directly estimating 
 a parameterized policy\, that maps states to probabilities over actions. T
 ypically\, these algorithms are built on the policy gradient theorem\, whi
 ch provides a simple form for the gradient of the policy optimization obje
 ctive. In practice\, however\, a key weighting in the gradient is dropped 
 for convenience\; despite this omission\, these widely used algorithms see
 m to perform quite well…
DTSTART;TZID=America/Vancouver:20200615T153000
DTEND;TZID=America/Vancouver:20200615T163000
LAST-MODIFIED:20210610T230539Z
LOCATION:Please register to receive the Zoom link
SUMMARY:Rethinking the Objective for Policy Optimization in Reinforcement L
 earning - Martha White\, Associate Professor\, University of Alberta
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/rethinking-objective-policy-optimization-rei
 nforcement-learning-martha-white-associate
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