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.