[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar Fri January 30th, 15:00,

Dumitru Erhan erhandum at iro.umontreal.ca
Lun 26 Jan 12:23:31 EST 2009


This week's seminar (see
http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):

New architectures and algorithms for solving deep-memory POMDPs

by Daan Wierstra,
IDSIA, Switzerland

Location: Pavillon André-Aisenstadt (UdeM), room 3195
Time: January 30th 2009, 15h00

Reinforcement learning in partially observable environments is a
difficult problem, and appears to be intractable for classical
reinforcement learning methods. We present two new approaches for
dealing with partial observability. One pertains to searching the
space of memory cell architectures in order to better capture
long-term time dependencies. The second approach involves the use of
natural gradients in stochastic search as an alternative to
conventional evolutionary methods.


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