Speaker
Bert Kappen
(University of Nijmegen, The Netherlands)
Description
Intelligent systems, whether natural or artificial, must act
in a world that is highly unpredictable. To plan actions
with uncertainty is a stochastic optimal control problem.
However, there are two fundamental problems: the optimal
control solution is intractable to compute and intractable
to represent due the non-trivial state dependence of the
optimal control. This has prevented large scale
application of stochastic optimal control theory sofar.
The path integral control theory describes a class of
control problems whose solution can be computed as
an inference computation. In this presentation we formalize
the intuitive notion that the efficiency of the inference
computation
is related to the proximity of the sampling control to the
optimal control. Secondly, we show new results that allow
approximate computation of state dependent optimal controls
in terms of basis functions. We illustrate the results on a
few examples.
