[Lisa_seminaires] Reminder: UdeM-McGill-MITACS machine learning seminar Mon, June 22nd,

Dumitru Erhan dumitru.erhan at umontreal.ca
Lun 22 Juin 07:46:20 EDT 2009


Here's a reminder of today's talk and a new abstract by Andrew Ng (the
topic has changed!):

Reinforcement learning and apprenticeship learning for robotic control

by Andrew Y. Ng

Location: Pavillon André-Aisenstadt (UdeM), room 3195
Time: June 22nd, 13h30

Drawing from examples in autonomous helicopter flight, legged robot
locomotion, and autonomous driving, in this talk I’ll describe a class
of reinforcement learning and apprenticeship learning
algorithms—methods which learn by watching an expert demonstration of
a task—for robotic control.  Specifically, we’ll see that for many
robots it Is challenging to develop a controller because (i) It is
hard to write down, in closed form, a formal specification of the
control task (for example, what is the cost function for "driving
well"?), and (ii) It is difficult to learn good models of the robot's
dynamics. I’ll present formal results showing how apprenticeship
learning methods, when given access to a human demonstration of a
task, can be used to efficiently address these problem.  Further, I’ll
present results on the application of these ideas to controlling
several different robots.

Bio:

Andrew Ng is an Assistant Professor of Computer Science at Stanford
University.  His research interests include machine learning,
reinforcement learning/control, and broad-competence AI.  His group has
won best paper/best student paper awards at ACL, CEAS, 3DRR and ICML.  He
is also a recipient of the Alfred P. Sloan Fellowship, and the IJCAI 2009
Computers and Thought award.


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