Tomorrow'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 …
[View More]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.
[View Less]
Begin forwarded message:
> From: Sergiy Yakovenko <sergiy.yakovenko(a)gmail.com>
> Date: January 22, 2009 9:02:20 PM EST (CA)
> To: Undisclosed-recipients: <>;
> Cc: Timothy Lillicrap <tim(a)biomed.queensu.ca>
> Subject: next math-neuro: 27th Jan (Tuesday)
>
> Dear Math-Neuro group,
>
> Our next Math-Neuro meeting will be next week on 27th of January (Tuesday), 4-6 PM, room 4106 of Pav. P.-G. Desmarais. Timothy Lillicrap of Queen's University will …
[View More]present an exciting talk entitled "How do the properties of the musculoskeletal system shape the activity of motor cortex neurons?"
>
> Regards,
> Sergiy & Paul
>
> Sergiy Yakovenko
> Département de physiologie
> Université de Montréal
> Tel: (514) 343-6111 ext 3333
>
> DIRECTIONS:
> http://maps.google.com/maps/ms?client=safari&oe=UTF-8&ie=UTF8&hl=en&t=k&lci…
>
>
>
>
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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 …
[View More]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.
[View Less]