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

Hugo Larochelle larocheh at iro.umontreal.ca
Ven 29 Fév 15:53:02 EST 2008


Next 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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