  We derive an algorithm in the spirit of Rogers and Davis & Burstein that
leads to upper bounds for stochastic control problems. Our bounds complement
lower biased estimates recently obtained in the work of Guyon &
Henry-Labord\`ere. We evaluate our estimates in numerical examples motivated
from mathematical finance.
