[Lisa_seminaires] REMINDER: UdeM-McGill-MITACS machine learning seminar Tues March 4th, 10:00am, MC437

Hugo Larochelle larocheh at iro.umontreal.ca
Lun 3 Mar 11:58:47 EST 2008


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


A Stochastic Algorithm for Partially Observable Markov Decision
Processes (POMDPs)


by Francois Laviolette,
Département d’Informatique
Université Laval

Location: McConnell Engineering Building (McGill), room 437
Time: March 4th 2008, 10h00

We introduce a new backup operator for point-based POMDP’s algorithms
which performs a look-ahead search at depth greater than one. We
apply this operator into a new algorithm, called Stochastic Search
Value Iteration (SSVI). This new algorithm relies on stochastic
explo- ration of the environment in order to update the value
function. The un- derlying ideas are very similar to temporal
difference learning algorithms for MDPs. In particular, SSVI takes
advantage of a soft-max actions se- lection function and of the
random character of the environment itself. This is in opposition
with existing POMDP’s point-based algorithms. Empirical results show
that our algorithm is very competitive on usual benchmark problems.
This suggests that stochastic algorithms are an alternative for
solving large POMDPs.



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