An efficient approach to stochastic optimal control
Bert Kappen, Radboud University Nijmegen, the Netherlands
Stochastic optimal control theory is a principled approach to compute
optimal actions with delayed rewards. The use of this approach in AI
and machine learning has been limited due to the computational
intractabilities. In this talk, I introduce a class of control
problems where the intractabilities appear as the computation of a
partition sum, as in a statistical mechanical system. This opens the
possibility to study phase transitions and to apply exisiting
approximation methods such as BP and the variational method to optimal
control theory. The talk gives a gentle introduction into control
theory and illustrates these new phenomena with a number of examples.
