  In this paper, a tractable methodology is proposed to approximate stochastic
optimal feedback treatment in the context of mixed immuno-chemo therapy of
cancer. The method uses a fixed-point value iteration that approximately solves
a stochastic dynamic programming-like equation. It is in particular shown that
the introduction of a variance-related penalty in the latter induces better
results that cope with the consequences of softening the health safety
constraints in the cost function. The convergence of the value function
iteration is revisited in the presence of the variance related term. The
implementation involves some Machine Learning tools in order to represent the
optimal function and to perform complexity reduction by clustering.
Quantitative illustration is given using a commonly used model of combined
therapy involving twelve highly uncertain parameters.
