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X-WR-RELCALID:efc09d74-9c93-479e-a94f-485231ddccde
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X-WR-CALNAME:The Provable Effectiveness of Policy Gradient Methods in Reinf
 orcement Learning and Controls - Sham Kakade\, Professor\, University of W
 ashington\; Microsoft Research
BEGIN:VTIMEZONE
TZID:America/Vancouver
TZUNTIL:20231105T090000Z
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DTSTART:20201101T020000
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RDATE:20211107T020000
RDATE:20221106T020000
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DTSTAMP:20251223T225738Z
CLASS:PUBLIC
CREATED:20210909T212713Z
DESCRIPTION:Please register for this event here. Abstract: Reinforcement le
 arning is the dominant paradigm for how an agent learns to interact with t
 he world in order to achieve some long term objectives. Here\, policy grad
 ient methods are among the most effective methods in challenging reinforce
 ment learning problems\, due to that they: are applicable to any different
 iable policy parameterization\; admit easy extensions to function approxim
 ation\; easily incorporate structured state and action spaces\; are easy t
 o implement in a simulation based\, model-free manner. However\, little is
  known about even their…
DTSTART;TZID=America/Vancouver:20210927T130000
DTEND;TZID=America/Vancouver:20210927T140000
LAST-MODIFIED:20210909T214657Z
LOCATION:Please register to receive the Zoom link
SUMMARY:The Provable Effectiveness of Policy Gradient Methods in Reinforcem
 ent Learning and Controls - Sham Kakade\, Professor\, University of Washin
 gton\; Microsoft Research
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/provable-effectiveness-policy-gradient-metho
 ds-reinforcement-learning-and-controls-sham
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